DETAILED ACTION
Claims 1-19 are currently pending and have been examined in this application. This is the first action on the merits.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This communication is in response to the application filed 06/23/2025.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “recognition unit,” “action planning unit,” “recognition action control unit” in Claim 1 and repeated throughout; “position information acquisition unit” in Claim 3 and repeated throughout; “map information accumulation unit” in claim 4 and repeated throughout.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof. The above limitations will be understood to include any hardware or software capable of the claimed functionality (Spec Para 0289)..
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 2 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 2 recites the limitation "the recognition algorithms" and “the action planning algorithms” whereas in Claim 1 only a singular one of each of “an action planning algorithm” and “a recognition algorithm”. As such, there is insufficient antecedent basis for these limitations in the claim.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 19 rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter because Claim 19 refers to " A program for causing a computer to function…", however a computer program per se (often referred to as "software per se") when claimed as a product without any structural recitations is not directed to any of the statutory categories. Appropriate correction is required.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
Claim(s) 1-3, 10-12, 17-19 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Takemura (US20110205042).
Claim 1:
Takemura explicitly teaches:
An information processing apparatus comprising: a recognition unit that has a recognition algorithm for recognizing an obstacle and recognizes the obstacle by the recognition algorithm on a basis of sensor information;
(Takemura) – “The road-environment recognition image processing device includes a processing section 100 for processing an image from a measurement device 101 such as the camera or the radar, an own-vehicle traveling information section 201, and a control/alarm determining section 300. The processing section 100 processes the information from a device such as the camera or the radar; detects a situation of the road; and make determination. For example, the processing section 100 receives from the measurement device the information for detecting and determining an obstacle including a human or an alternate vehicle in terms of traveling; a white line or a road shoulder, and further, a mark or a signal etc.; or a road state etc. such as a curve.” (Para 0077)
Examiner Note: Per BRI, any defined form of processing may correspond with an algorithm.
an action planning unit that has an action planning algorithm for planning a travel route, and plans the travel route of a mobile apparatus by the action planning algorithm; and
(Takemura) – “This embodiment is an example in which the camera imaging section 101 is used as a representative. The processing section 100 is explained by a content in which image processing is executed by using the image imaged by the camera imaging section 101. However, with the same way of thinking, the processing section 100 is also applicable to the processing by use of other information. Based on the determination of the control/alarm determining section 300, a multi-application controlling section 111 determines to execute the processing cycles or operation of the respective functions (described as "ON"); to stop execution (described as "OFF"); or the like. Based on the result, a multi-application executing section 121 executes the respective functions. In the case where a recognition result by the on-vehicle camera is used to execute vehicle control such as collision avoidance or traffic-lane departure avoidance, it is desirable for a recognition processing cycle of the camera to be shorter, compared with the time of normal traveling. Contrarily, at the time of normal traveling having been prepared for the emergency as described above, it is not so much necessary to recognize an external world of the vehicle with the short processing cycle as at the time of emergency. The multi-application controlling section 111 controls the processing cycle or ON/OFF of execution operation, thereby enabling to execute the recognition processing by the camera concerning the collision avoidance or the traffic-lane departure avoidance at the time of emergency with the short processing cycle. Meanwhile, at the time of normal traveling of the vehicle, it is possible to operate the application which pursues the convenience by means of time sharing, and to utilize limited processing capacity of a processor more effectively.” (Para 0078)
a recognition action control unit that performs control to switch at least any of the recognition algorithm and the action planning algorithm on a basis of a traveling environment of the mobile apparatus.
(Takemura) – “First, constituent elements will be described by use of FIG. 1. As one embodiment for executing, by means of time sharing, the plurality of applications (hereinafter, may be described as "multi-applications") capable of dynamically switching the processing cycles of the application based on determination at the normal time or at the time of emergency in vehicle traveling, shown is a configuration of an image processing device (hereinafter, may be described as a "road-environment recognition image processing device") for executing environment recognition of a road by use of information from the on-vehicle camera.” (Para 0076)
“This embodiment is an example in which the camera imaging section 101 is used as a representative. The processing section 100 is explained by a content in which image processing is executed by using the image imaged by the camera imaging section 101. However, with the same way of thinking, the processing section 100 is also applicable to the processing by use of other information. Based on the determination of the control/alarm determining section 300, a multi-application controlling section 111 determines to execute the processing cycles or operation of the respective functions (described as "ON"); to stop execution (described as "OFF"); or the like. Based on the result, a multi-application executing section 121 executes the respective functions. In the case where a recognition result by the on-vehicle camera is used to execute vehicle control such as collision avoidance or traffic-lane departure avoidance, it is desirable for a recognition processing cycle of the camera to be shorter, compared with the time of normal traveling. Contrarily, at the time of normal traveling having been prepared for the emergency as described above, it is not so much necessary to recognize an external world of the vehicle with the short processing cycle as at the time of emergency. The multi-application controlling section 111 controls the processing cycle or ON/OFF of execution operation, thereby enabling to execute the recognition processing by the camera concerning the collision avoidance or the traffic-lane departure avoidance at the time of emergency with the short processing cycle. Meanwhile, at the time of normal traveling of the vehicle, it is possible to operate the application which pursues the convenience by means of time sharing, and to utilize limited processing capacity of a processor more effectively.” (Para 0078)
“The switching of execution conditions of the application as described above is based on the switching of the traveling environments from the general-road traveling to the expressway traveling. The switching of the traveling environments in this way leads to the change of the applications of to be executed, and leads to the change of the priority or other execution conditions. Methods for detecting the switching of the traveling environments include, for example, the switching of a gear change, the change in vehicle speed, passing through a tollgate of a toll road such as the expressway, or the information from the car navigation system.” (Para 0091)
Claim 2:
Takemura teaches the respective limitations of Claim 1. Takemura further teaches:
further comprising a storage unit that stores at least any of a plurality of the recognition algorithms according to the traveling environment and a plurality of the action planning algorithms according to the traveling environment, wherein the recognition action control unit searches the storage unit for at least any of the recognition algorithm and the action planning algorithm corresponding to the traveling environment, and performs control to switch at least any of the recognition algorithm of the recognition unit and the action planning algorithm of the action planning unit to the searched recognition algorithm and the searched action planning algorithm.
(Takemura) – “First, constituent elements will be described by use of FIG. 1. As one embodiment for executing, by means of time sharing, the plurality of applications (hereinafter, may be described as "multi-applications") capable of dynamically switching the processing cycles of the application based on determination at the normal time or at the time of emergency in vehicle traveling, shown is a configuration of an image processing device (hereinafter, may be described as a "road-environment recognition image processing device") for executing environment recognition of a road by use of information from the on-vehicle camera.” (Para 0076)
“This embodiment is an example in which the camera imaging section 101 is used as a representative. The processing section 100 is explained by a content in which image processing is executed by using the image imaged by the camera imaging section 101. However, with the same way of thinking, the processing section 100 is also applicable to the processing by use of other information. Based on the determination of the control/alarm determining section 300, a multi-application controlling section 111 determines to execute the processing cycles or operation of the respective functions (described as "ON"); to stop execution (described as "OFF"); or the like. Based on the result, a multi-application executing section 121 executes the respective functions. In the case where a recognition result by the on-vehicle camera is used to execute vehicle control such as collision avoidance or traffic-lane departure avoidance, it is desirable for a recognition processing cycle of the camera to be shorter, compared with the time of normal traveling. Contrarily, at the time of normal traveling having been prepared for the emergency as described above, it is not so much necessary to recognize an external world of the vehicle with the short processing cycle as at the time of emergency. The multi-application controlling section 111 controls the processing cycle or ON/OFF of execution operation, thereby enabling to execute the recognition processing by the camera concerning the collision avoidance or the traffic-lane departure avoidance at the time of emergency with the short processing cycle. Meanwhile, at the time of normal traveling of the vehicle, it is possible to operate the application which pursues the convenience by means of time sharing, and to utilize limited processing capacity of a processor more effectively.” (Para 0078)
“The switching of execution conditions of the application as described above is based on the switching of the traveling environments from the general-road traveling to the expressway traveling. The switching of the traveling environments in this way leads to the change of the applications of to be executed, and leads to the change of the priority or other execution conditions. Methods for detecting the switching of the traveling environments include, for example, the switching of a gear change, the change in vehicle speed, passing through a tollgate of a toll road such as the expressway, or the information from the car navigation system.” (Para 0091)
“FIG. 22 shows another embodiment. The same reference numerals as those of other figures denote the same functions or the same operations. This embodiment is configured such that the processing function of the task table is included in the measurement device 101 of the camera. However, the above-mentioned processing function may be executed by a computer for control in another device. In the traveling-information processing section 130 of the own vehicle; based on vehicle-speed information 131 or steering-angle information 132; in addition, by use of image information or radar information, as necessary; based on the calculated data or the data having been preliminarily stored in accordance with a traveling state, both having been previously explained; the traveling environment, a risk of collision, or a need for paying attention to the obstacle or the pedestrian is selected in a control-prediction determining section 142 with reference to the task table, as previously explained. For example, the task table of FIG. 21 is selected from many task tables which have been preliminarily stored. In Step 2214, as previously explained, based on the task table, the priority of the execution is determined, and starting from the task having the high priority, the execution is made in Step 2216. The application targeted for the execution is the application stored in a storing section 2216. Based on this execution, when a new risk is predicted, or a change in the traveling environment is detected, in the control-prediction determining section 142, the task table corresponding to such prediction or such detection is selected from the task tables which have been preliminarily stored. In accordance with the selected task table, in the above-mentioned Step 2214, the application to be preferentially executed is selected. In Step 2216, the selected application is executed. The task table having been stored may be used, without any change, in the control-prediction determining section 142. However, a part of the stored data may be changed in line with predicted or detected content.” (Para 0129)
Claim 3:
Takemura teaches the respective limitations of Claim 1. Takemura further teaches:
further comprising a position information acquisition unit that acquires current position information of the information processing apparatus, wherein the recognition action control unit specifies the traveling environment on a basis of the position information, and performs control to switch at least any of the recognition algorithm and the action planning algorithm on a basis of the specified traveling environment.
(Takemura) – “First, constituent elements will be described by use of FIG. 1. As one embodiment for executing, by means of time sharing, the plurality of applications (hereinafter, may be described as "multi-applications") capable of dynamically switching the processing cycles of the application based on determination at the normal time or at the time of emergency in vehicle traveling, shown is a configuration of an image processing device (hereinafter, may be described as a "road-environment recognition image processing device") for executing environment recognition of a road by use of information from the on-vehicle camera.” (Para 0076)
“The road-environment recognition image processing device includes a processing section 100 for processing an image from a measurement device 101 such as the camera or the radar, an own-vehicle traveling information section 201, and a control/alarm determining section 300. The processing section 100 processes the information from a device such as the camera or the radar; detects a situation of the road; and make determination. For example, the processing section 100 receives from the measurement device the information for detecting and determining an obstacle including a human or an alternate vehicle in terms of traveling; a white line or a road shoulder, and further, a mark or a signal etc.; or a road state etc. such as a curve.” (Para 0077)
“This embodiment is an example in which the camera imaging section 101 is used as a representative. The processing section 100 is explained by a content in which image processing is executed by using the image imaged by the camera imaging section 101. However, with the same way of thinking, the processing section 100 is also applicable to the processing by use of other information. Based on the determination of the control/alarm determining section 300, a multi-application controlling section 111 determines to execute the processing cycles or operation of the respective functions (described as "ON"); to stop execution (described as "OFF"); or the like. Based on the result, a multi-application executing section 121 executes the respective functions. In the case where a recognition result by the on-vehicle camera is used to execute vehicle control such as collision avoidance or traffic-lane departure avoidance, it is desirable for a recognition processing cycle of the camera to be shorter, compared with the time of normal traveling. Contrarily, at the time of normal traveling having been prepared for the emergency as described above, it is not so much necessary to recognize an external world of the vehicle with the short processing cycle as at the time of emergency. The multi-application controlling section 111 controls the processing cycle or ON/OFF of execution operation, thereby enabling to execute the recognition processing by the camera concerning the collision avoidance or the traffic-lane departure avoidance at the time of emergency with the short processing cycle. Meanwhile, at the time of normal traveling of the vehicle, it is possible to operate the application which pursues the convenience by means of time sharing, and to utilize limited processing capacity of a processor more effectively.” (Para 0078)
“The own-vehicle traveling information section 201 transmits the information regarding the traveling of the own vehicle via an on-vehicle communication network to the processing section 100. The own-vehicle traveling information section 201 includes the vehicle-speed sensor 211, the rudder-angle sensor 212, a turning indicator 213, application operation 214, and the car navigation system 215, of the own vehicle.” (Para 0085)
“The switching of execution conditions of the application as described above is based on the switching of the traveling environments from the general-road traveling to the expressway traveling. The switching of the traveling environments in this way leads to the change of the applications of to be executed, and leads to the change of the priority or other execution conditions. Methods for detecting the switching of the traveling environments include, for example, the switching of a gear change, the change in vehicle speed, passing through a tollgate of a toll road such as the expressway, or the information from the car navigation system.” (Para 0091)
“In the case where in the lane recognition application 130 or the pedestrian detection application 140, the priority is placed on positional accuracy of the detection result, or the farther distance is the detection object, it is desirable to use the image having the high resolution. However, it is likely that calculation time will increase due to the use of the high-resolution image, and because of its processing load, an emphasis, such as not being in time for the processing cycle, will be given. The resolution is adjusted before executing the application in accordance with the necessary resolution in response to the conditions such as detection position accuracy, a detection object distance, the processing cycle. For example, in the case of assuming the traffic-lane departure avoidance, a lateral position of the lane with respect to the vehicle or a yaw angle requires the recognition accuracy in some degree, however, a curvature value in the distance, a lane position in the distance farther from a prediction position of the traffic-lane departure, or the like is not so much important. Under the circumstance where the vehicle is controlled for the traffic-lane departure avoidance, the image having the low resolution is used to reduce processing costs, whereby the update cycle of the lateral position of the lane or the yaw angle to be used for the vehicle control is hastened, and the higher-level vehicle control is actualized.” (Para 0096)
Claim 10:
Takemura teaches the respective limitations of Claim 1. Takemura further teaches:
wherein the recognition action control unit specifies the traveling environment on a basis of date and time or weather, and performs control to switch at least any of the recognition algorithm and the action planning algorithm on a basis of the specified traveling environment.
(Takemura) – “In addition, even in the case where the driving in which the priority is placed on the economic efficiency is desired, when the environmental conditions of rainy weather, snow, and dense fog are detected from camera information, the task table shown in FIG. 31 is selected in Step S3-2. By this way, a detailed response is enabled, and the safety is improved.” (Para 0133)
“First, constituent elements will be described by use of FIG. 1. As one embodiment for executing, by means of time sharing, the plurality of applications (hereinafter, may be described as "multi-applications") capable of dynamically switching the processing cycles of the application based on determination at the normal time or at the time of emergency in vehicle traveling, shown is a configuration of an image processing device (hereinafter, may be described as a "road-environment recognition image processing device") for executing environment recognition of a road by use of information from the on-vehicle camera.” (Para 0076)
“The road-environment recognition image processing device includes a processing section 100 for processing an image from a measurement device 101 such as the camera or the radar, an own-vehicle traveling information section 201, and a control/alarm determining section 300. The processing section 100 processes the information from a device such as the camera or the radar; detects a situation of the road; and make determination. For example, the processing section 100 receives from the measurement device the information for detecting and determining an obstacle including a human or an alternate vehicle in terms of traveling; a white line or a road shoulder, and further, a mark or a signal etc.; or a road state etc. such as a curve.” (Para 0077)
“This embodiment is an example in which the camera imaging section 101 is used as a representative. The processing section 100 is explained by a content in which image processing is executed by using the image imaged by the camera imaging section 101. However, with the same way of thinking, the processing section 100 is also applicable to the processing by use of other information. Based on the determination of the control/alarm determining section 300, a multi-application controlling section 111 determines to execute the processing cycles or operation of the respective functions (described as "ON"); to stop execution (described as "OFF"); or the like. Based on the result, a multi-application executing section 121 executes the respective functions. In the case where a recognition result by the on-vehicle camera is used to execute vehicle control such as collision avoidance or traffic-lane departure avoidance, it is desirable for a recognition processing cycle of the camera to be shorter, compared with the time of normal traveling. Contrarily, at the time of normal traveling having been prepared for the emergency as described above, it is not so much necessary to recognize an external world of the vehicle with the short processing cycle as at the time of emergency. The multi-application controlling section 111 controls the processing cycle or ON/OFF of execution operation, thereby enabling to execute the recognition processing by the camera concerning the collision avoidance or the traffic-lane departure avoidance at the time of emergency with the short processing cycle. Meanwhile, at the time of normal traveling of the vehicle, it is possible to operate the application which pursues the convenience by means of time sharing, and to utilize limited processing capacity of a processor more effectively.” (Para 0078)
Claim 11:
Takemura teaches the respective limitations of Claim 1. Takemura further teaches:
wherein the recognition action control unit performs control to switch the recognition algorithm of the recognition unit to a recognition algorithm for turning on processing of recognizing a predetermined obstacle in a case where an existence probability of the predetermined obstacle is higher than a predetermined value in accordance with the traveling environment, and performs control to switch the recognition algorithm of the recognition unit to a recognition algorithm for turning off the processing of recognizing the predetermined obstacle in a case where the existence probability of the predetermined obstacle is lower than the predetermined value in accordance with the traveling environment.
(Takemura) – “First, constituent elements will be described by use of FIG. 1. As one embodiment for executing, by means of time sharing, the plurality of applications (hereinafter, may be described as "multi-applications") capable of dynamically switching the processing cycles of the application based on determination at the normal time or at the time of emergency in vehicle traveling, shown is a configuration of an image processing device (hereinafter, may be described as a "road-environment recognition image processing device") for executing environment recognition of a road by use of information from the on-vehicle camera.” (Para 0076)
“The road-environment recognition image processing device includes a processing section 100 for processing an image from a measurement device 101 such as the camera or the radar, an own-vehicle traveling information section 201, and a control/alarm determining section 300. The processing section 100 processes the information from a device such as the camera or the radar; detects a situation of the road; and make determination. For example, the processing section 100 receives from the measurement device the information for detecting and determining an obstacle including a human or an alternate vehicle in terms of traveling; a white line or a road shoulder, and further, a mark or a signal etc.; or a road state etc. such as a curve.” (Para 0077)
“This embodiment is an example in which the camera imaging section 101 is used as a representative. The processing section 100 is explained by a content in which image processing is executed by using the image imaged by the camera imaging section 101. However, with the same way of thinking, the processing section 100 is also applicable to the processing by use of other information. Based on the determination of the control/alarm determining section 300, a multi-application controlling section 111 determines to execute the processing cycles or operation of the respective functions (described as "ON"); to stop execution (described as "OFF"); or the like. Based on the result, a multi-application executing section 121 executes the respective functions. In the case where a recognition result by the on-vehicle camera is used to execute vehicle control such as collision avoidance or traffic-lane departure avoidance, it is desirable for a recognition processing cycle of the camera to be shorter, compared with the time of normal traveling. Contrarily, at the time of normal traveling having been prepared for the emergency as described above, it is not so much necessary to recognize an external world of the vehicle with the short processing cycle as at the time of emergency. The multi-application controlling section 111 controls the processing cycle or ON/OFF of execution operation, thereby enabling to execute the recognition processing by the camera concerning the collision avoidance or the traffic-lane departure avoidance at the time of emergency with the short processing cycle. Meanwhile, at the time of normal traveling of the vehicle, it is possible to operate the application which pursues the convenience by means of time sharing, and to utilize limited processing capacity of a processor more effectively.” (Para 0078)
“A pedestrian detection application 140 is also classified into the pedestrian detection application 140 (an essential version 1140) whose operational assurance is performed with respect to a distance which allows collision to be avoided in consideration of a braking distance in response to vehicle speed; and the pedestrian detection application 140 (an additional version 2140) which executes detection processing also with respect to a pedestrian candidate in the farther distance. The pedestrian detection application 140 (the essential version 1140) for securing the safety assumes that a pedestrian be in near distance to some extent, and thus, is set so as to use the image having resolution to some extent, rather than using the image having high resolution, and to place priority on more hastening the update cycle of a detection result. Contrarily, the pedestrian detection application 140 (the additional version 2140) targets the pedestrian in the farther distance, and thus, uses the image having high resolution.” (Para 0082)
“Also regarding a vehicle detection application 150, the vehicle detection application 150 (an essential version 1150), which executes the image processing with respect to a vehicle candidate required for avoiding the collision in consideration of relative speed or the distance to a leading vehicle and of an own-vehicle rudder angle, is classified into the essential function section 1100. The vehicle detection application 150 (an additional version 2150), which makes the vehicle in the farther distance as the detection object with an aim of detecting the vehicle earlier; improving a detection rate; or controlling an inter-vehicle distance etc., is classified into the additional function section 2100. A policy concerning the resolution or the processing cycle is similar to the policy of the pedestrian detection application 140.” (Para 0083)
Examiner Note: Per BRI, any determination of a valid detection of a specific object may correspond with a predetermined obstacle in a case where an existence probability of the predetermined obstacle is higher than a predetermined value.
Claim 12:
Takemura teaches the respective limitations of Claim 1. Takemura further teaches:
wherein the recognition action control unit performs control to switch the recognition algorithm of the recognition unit to a recognition algorithm in which a frequency of processing of recognizing a predetermined obstacle is higher than a predetermined frequency in a case where an existence probability of the predetermined obstacle is higher than a predetermined value in accordance with the traveling environment, and performs control to switch the recognition algorithm of the recognition unit to a recognition algorithm in which the frequency of processing of recognizing the predetermined obstacle is lower than the predetermined frequency in a case where the existence probability of the predetermined obstacle is lower than the predetermined value in accordance with the traveling environment.
(Takemura) – “First, constituent elements will be described by use of FIG. 1. As one embodiment for executing, by means of time sharing, the plurality of applications (hereinafter, may be described as "multi-applications") capable of dynamically switching the processing cycles of the application based on determination at the normal time or at the time of emergency in vehicle traveling, shown is a configuration of an image processing device (hereinafter, may be described as a "road-environment recognition image processing device") for executing environment recognition of a road by use of information from the on-vehicle camera.” (Para 0076)
“The road-environment recognition image processing device includes a processing section 100 for processing an image from a measurement device 101 such as the camera or the radar, an own-vehicle traveling information section 201, and a control/alarm determining section 300. The processing section 100 processes the information from a device such as the camera or the radar; detects a situation of the road; and make determination. For example, the processing section 100 receives from the measurement device the information for detecting and determining an obstacle including a human or an alternate vehicle in terms of traveling; a white line or a road shoulder, and further, a mark or a signal etc.; or a road state etc. such as a curve.” (Para 0077)
“This embodiment is an example in which the camera imaging section 101 is used as a representative. The processing section 100 is explained by a content in which image processing is executed by using the image imaged by the camera imaging section 101. However, with the same way of thinking, the processing section 100 is also applicable to the processing by use of other information. Based on the determination of the control/alarm determining section 300, a multi-application controlling section 111 determines to execute the processing cycles or operation of the respective functions (described as "ON"); to stop execution (described as "OFF"); or the like. Based on the result, a multi-application executing section 121 executes the respective functions. In the case where a recognition result by the on-vehicle camera is used to execute vehicle control such as collision avoidance or traffic-lane departure avoidance, it is desirable for a recognition processing cycle of the camera to be shorter, compared with the time of normal traveling. Contrarily, at the time of normal traveling having been prepared for the emergency as described above, it is not so much necessary to recognize an external world of the vehicle with the short processing cycle as at the time of emergency. The multi-application controlling section 111 controls the processing cycle or ON/OFF of execution operation, thereby enabling to execute the recognition processing by the camera concerning the collision avoidance or the traffic-lane departure avoidance at the time of emergency with the short processing cycle. Meanwhile, at the time of normal traveling of the vehicle, it is possible to operate the application which pursues the convenience by means of time sharing, and to utilize limited processing capacity of a processor more effectively.” (Para 0078)
“A pedestrian detection application 140 is also classified into the pedestrian detection application 140 (an essential version 1140) whose operational assurance is performed with respect to a distance which allows collision to be avoided in consideration of a braking distance in response to vehicle speed; and the pedestrian detection application 140 (an additional version 2140) which executes detection processing also with respect to a pedestrian candidate in the farther distance. The pedestrian detection application 140 (the essential version 1140) for securing the safety assumes that a pedestrian be in near distance to some extent, and thus, is set so as to use the image having resolution to some extent, rather than using the image having high resolution, and to place priority on more hastening the update cycle of a detection result. Contrarily, the pedestrian detection application 140 (the additional version 2140) targets the pedestrian in the farther distance, and thus, uses the image having high resolution.” (Para 0082)
“Also regarding a vehicle detection application 150, the vehicle detection application 150 (an essential version 1150), which executes the image processing with respect to a vehicle candidate required for avoiding the collision in consideration of relative speed or the distance to a leading vehicle and of an own-vehicle rudder angle, is classified into the essential function section 1100. The vehicle detection application 150 (an additional version 2150), which makes the vehicle in the farther distance as the detection object with an aim of detecting the vehicle earlier; improving a detection rate; or controlling an inter-vehicle distance etc., is classified into the additional function section 2100. A policy concerning the resolution or the processing cycle is similar to the policy of the pedestrian detection application 140.” (Para 0083)
Examiner Note: Per BRI, short processing cycle corresponds with frequency of processing of recognizing a predetermined obstacle is higher than a predetermined frequency.
Claim 17:
Takemura teaches the respective limitations of Claim 1. Takemura further teaches:
wherein the recognition action control unit switches the action planning algorithm of the action planning unit and switches a parameter used in processing by the action planning algorithm in accordance with the traveling environment.
(Takemura) – “First, constituent elements will be described by use of FIG. 1. As one embodiment for executing, by means of time sharing, the plurality of applications (hereinafter, may be described as "multi-applications") capable of dynamically switching the processing cycles of the application based on determination at the normal time or at the time of emergency in vehicle traveling, shown is a configuration of an image processing device (hereinafter, may be described as a "road-environment recognition image processing device") for executing environment recognition of a road by use of information from the on-vehicle camera.” (Para 0076)
“The road-environment recognition image processing device includes a processing section 100 for processing an image from a measurement device 101 such as the camera or the radar, an own-vehicle traveling information section 201, and a control/alarm determining section 300. The processing section 100 processes the information from a device such as the camera or the radar; detects a situation of the road; and make determination. For example, the processing section 100 receives from the measurement device the information for detecting and determining an obstacle including a human or an alternate vehicle in terms of traveling; a white line or a road shoulder, and further, a mark or a signal etc.; or a road state etc. such as a curve.” (Para 0077)
“This embodiment is an example in which the camera imaging section 101 is used as a representative. The processing section 100 is explained by a content in which image processing is executed by using the image imaged by the camera imaging section 101. However, with the same way of thinking, the processing section 100 is also applicable to the processing by use of other information. Based on the determination of the control/alarm determining section 300, a multi-application controlling section 111 determines to execute the processing cycles or operation of the respective functions (described as "ON"); to stop execution (described as "OFF"); or the like. Based on the result, a multi-application executing section 121 executes the respective functions. In the case where a recognition result by the on-vehicle camera is used to execute vehicle control such as collision avoidance or traffic-lane departure avoidance, it is desirable for a recognition processing cycle of the camera to be shorter, compared with the time of normal traveling. Contrarily, at the time of normal traveling having been prepared for the emergency as described above, it is not so much necessary to recognize an external world of the vehicle with the short processing cycle as at the time of emergency. The multi-application controlling section 111 controls the processing cycle or ON/OFF of execution operation, thereby enabling to execute the recognition processing by the camera concerning the collision avoidance or the traffic-lane departure avoidance at the time of emergency with the short processing cycle. Meanwhile, at the time of normal traveling of the vehicle, it is possible to operate the application which pursues the convenience by means of time sharing, and to utilize limited processing capacity of a processor more effectively.” (Para 0078)
Examiner Note: Parameter is recited with a high level of generality and may correspond with any aspect of processing.
Claim 18:
Takemura explicitly teaches:
An information processing method comprising steps of: recognizing an obstacle on a basis of sensor information by a recognition algorithm for recognizing the obstacle;
(Takemura) – “The road-environment recognition image processing device includes a processing section 100 for processing an image from a measurement device 101 such as the camera or the radar, an own-vehicle traveling information section 201, and a control/alarm determining section 300. The processing section 100 processes the information from a device such as the camera or the radar; detects a situation of the road; and make determination. For example, the processing section 100 receives from the measurement device the information for detecting and determining an obstacle including a human or an alternate vehicle in terms of traveling; a white line or a road shoulder, and further, a mark or a signal etc.; or a road state etc. such as a curve.” (Para 0077)
Examiner Note: Per BRI, any defined form of processing may correspond with an algorithm.
planning a travel route of a mobile apparatus by an action planning algorithm for planning the travel route; and
(Takemura) – “This embodiment is an example in which the camera imaging section 101 is used as a representative. The processing section 100 is explained by a content in which image processing is executed by using the image imaged by the camera imaging section 101. However, with the same way of thinking, the processing section 100 is also applicable to the processing by use of other information. Based on the determination of the control/alarm determining section 300, a multi-application controlling section 111 determines to execute the processing cycles or operation of the respective functions (described as "ON"); to stop execution (described as "OFF"); or the like. Based on the result, a multi-application executing section 121 executes the respective functions. In the case where a recognition result by the on-vehicle camera is used to execute vehicle control such as collision avoidance or traffic-lane departure avoidance, it is desirable for a recognition processing cycle of the camera to be shorter, compared with the time of normal traveling. Contrarily, at the time of normal traveling having been prepared for the emergency as described above, it is not so much necessary to recognize an external world of the vehicle with the short processing cycle as at the time of emergency. The multi-application controlling section 111 controls the processing cycle or ON/OFF of execution operation, thereby enabling to execute the recognition processing by the camera concerning the collision avoidance or the traffic-lane departure avoidance at the time of emergency with the short processing cycle. Meanwhile, at the time of normal traveling of the vehicle, it is possible to operate the application which pursues the convenience by means of time sharing, and to utilize limited processing capacity of a processor more effectively.” (Para 0078)
performing control to switch at least any of the recognition algorithm and the action planning algorithm on a basis of a traveling environment of the mobile apparatus.
(Takemura) – “First, constituent elements will be described by use of FIG. 1. As one embodiment for executing, by means of time sharing, the plurality of applications (hereinafter, may be described as "multi-applications") capable of dynamically switching the processing cycles of the application based on determination at the normal time or at the time of emergency in vehicle traveling, shown is a configuration of an image processing device (hereinafter, may be described as a "road-environment recognition image processing device") for executing environment recognition of a road by use of information from the on-vehicle camera.” (Para 0076)
“This embodiment is an example in which the camera imaging section 101 is used as a representative. The processing section 100 is explained by a content in which image processing is executed by using the image imaged by the camera imaging section 101. However, with the same way of thinking, the processing section 100 is also applicable to the processing by use of other information. Based on the determination of the control/alarm determining section 300, a multi-application controlling section 111 determines to execute the processing cycles or operation of the respective functions (described as "ON"); to stop execution (described as "OFF"); or the like. Based on the result, a multi-application executing section 121 executes the respective functions. In the case where a recognition result by the on-vehicle camera is used to execute vehicle control such as collision avoidance or traffic-lane departure avoidance, it is desirable for a recognition processing cycle of the camera to be shorter, compared with the time of normal traveling. Contrarily, at the time of normal traveling having been prepared for the emergency as described above, it is not so much necessary to recognize an external world of the vehicle with the short processing cycle as at the time of emergency. The multi-application controlling section 111 controls the processing cycle or ON/OFF of execution operation, thereby enabling to execute the recognition processing by the camera concerning the collision avoidance or the traffic-lane departure avoidance at the time of emergency with the short processing cycle. Meanwhile, at the time of normal traveling of the vehicle, it is possible to operate the application which pursues the convenience by means of time sharing, and to utilize limited processing capacity of a processor more effectively.” (Para 0078)
“The switching of execution conditions of the application as described above is based on the switching of the traveling environments from the general-road traveling to the expressway traveling. The switching of the traveling environments in this way leads to the change of the applications of to be executed, and leads to the change of the priority or other execution conditions. Methods for detecting the switching of the traveling environments include, for example, the switching of a gear change, the change in vehicle speed, passing through a tollgate of a toll road such as the expressway, or the information from the car navigation system.” (Para 0091)
Claim 19:
Takemura explicitly teaches:
A program for causing a computer to function as: a recognition unit that has a recognition algorithm for recognizing an obstacle and recognizes the obstacle by the recognition algorithm on a basis of sensor information;
(Takemura) – “The road-environment recognition image processing device includes a processing section 100 for processing an image from a measurement device 101 such as the camera or the radar, an own-vehicle traveling information section 201, and a control/alarm determining section 300. The processing section 100 processes the information from a device such as the camera or the radar; detects a situation of the road; and make determination. For example, the processing section 100 receives from the measurement device the information for detecting and determining an obstacle including a human or an alternate vehicle in terms of traveling; a white line or a road shoulder, and further, a mark or a signal etc.; or a road state etc. such as a curve.” (Para 0077)
Examiner Note: Per BRI, any defined form of processing may correspond with an algorithm.
an action planning unit that has an action planning algorithm for planning a travel route, and plans the travel route of a mobile apparatus by the action planning algorithm; and
(Takemura) – “This embodiment is an example in which the camera imaging section 101 is used as a representative. The processing section 100 is explained by a content in which image processing is executed by using the image imaged by the camera imaging section 101. However, with the same way of thinking, the processing section 100 is also applicable to the processing by use of other information. Based on the determination of the control/alarm determining section 300, a multi-application controlling section 111 determines to execute the processing cycles or operation of the respective functions (described as "ON"); to stop execution (described as "OFF"); or the like. Based on the result, a multi-application executing section 121 executes the respective functions. In the case where a recognition result by the on-vehicle camera is used to execute vehicle control such as collision avoidance or traffic-lane departure avoidance, it is desirable for a recognition processing cycle of the camera to be shorter, compared with the time of normal traveling. Contrarily, at the time of normal traveling having been prepared for the emergency as described above, it is not so much necessary to recognize an external world of the vehicle with the short processing cycle as at the time of emergency. The multi-application controlling section 111 controls the processing cycle or ON/OFF of execution operation, thereby enabling to execute the recognition processing by the camera concerning the collision avoidance or the traffic-lane departure avoidance at the time of emergency with the short processing cycle. Meanwhile, at the time of normal traveling of the vehicle, it is possible to operate the application which pursues the convenience by means of time sharing, and to utilize limited processing capacity of a processor more effectively.” (Para 0078)
a recognition action control unit that performs control to switch at least any of the recognition algorithm and the action planning algorithm on a basis of a traveling environment of the mobile apparatus.
(Takemura) – “First, constituent elements will be described by use of FIG. 1. As one embodiment for executing, by means of time sharing, the plurality of applications (hereinafter, may be described as "multi-applications") capable of dynamically switching the processing cycles of the application based on determination at the normal time or at the time of emergency in vehicle traveling, shown is a configuration of an image processing device (hereinafter, may be described as a "road-environment recognition image processing device") for executing environment recognition of a road by use of information from the on-vehicle camera.” (Para 0076)
“This embodiment is an example in which the camera imaging section 101 is used as a representative. The processing section 100 is explained by a content in which image processing is executed by using the image imaged by the camera imaging section 101. However, with the same way of thinking, the processing section 100 is also applicable to the processing by use of other information. Based on the determination of the control/alarm determining section 300, a multi-application controlling section 111 determines to execute the processing cycles or operation of the respective functions (described as "ON"); to stop execution (described as "OFF"); or the like. Based on the result, a multi-application executing section 121 executes the respective functions. In the case where a recognition result by the on-vehicle camera is used to execute vehicle control such as collision avoidance or traffic-lane departure avoidance, it is desirable for a recognition processing cycle of the camera to be shorter, compared with the time of normal traveling. Contrarily, at the time of normal traveling having been prepared for the emergency as described above, it is not so much necessary to recognize an external world of the vehicle with the short processing cycle as at the time of emergency. The multi-application controlling section 111 controls the processing cycle or ON/OFF of execution operation, thereby enabling to execute the recognition processing by the camera concerning the collision avoidance or the traffic-lane departure avoidance at the time of emergency with the short processing cycle. Meanwhile, at the time of normal traveling of the vehicle, it is possible to operate the application which pursues the convenience by means of time sharing, and to utilize limited processing capacity of a processor more effectively.” (Para 0078)
“The switching of execution conditions of the application as described above is based on the switching of the traveling environments from the general-road traveling to the expressway traveling. The switching of the traveling environments in this way leads to the change of the applications of to be executed, and leads to the change of the priority or other execution conditions. Methods for detecting the switching of the traveling environments include, for example, the switching of a gear change, the change in vehicle speed, passing through a tollgate of a toll road such as the expressway, or the information from the car navigation system.” (Para 0091)
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 4-9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Takemura (US20110205042) in view of Stenneth (US20220063639).
Claim 4:
Takemura teaches the respective limitations of Claim 3. Takemura does not explicitly teach the following limitations in whole. Stenneth, in the same field of endeavor of vehicle control, teaches:
further comprising a map information accumulation unit that accumulates map information in which a traveling environment is recorded for each position information, wherein the recognition action control unit specifies the traveling environment corresponding to the position information on a basis of the map information accumulated in the map information accumulation unit.
(Stenneth) – “With reference to FIG. 1, a system 100 configured to predict autonomous transition regions using historical information is depicted, in accordance with one or more embodiments of the present disclosure. In the illustrated embodiment, the system 100 includes an apparatus 102 and a map database 104. As described further below, the apparatus 102 is configured in accordance with an example embodiment of the present disclosure to assist navigation of a vehicle and/or to autonomous driving for a vehicle. The apparatus 102 can be embodied by any of a wide variety of computing devices including, for example, a computer system of a vehicle, a vehicle system of a vehicle, a navigation system of a vehicle, a control system of a vehicle, an electronic control unit of a vehicle, an autonomous vehicle control system (e.g., an autonomous-driving control system) of a vehicle, a mapping system of a vehicle, an Advanced Driver Assistance System module (ADAS of a vehicle), or any other type of computing device carried by or remote from the vehicle including, for example, a server or a distributed network of computing devices.” (Para 0047)
“In one or more embodiments, the apparatus 102, such as the processing circuitry 106, can be configured to generate one or more road links (e.g., one or more map-matched road links) for the predicted autonomous transition data for the second geographic area to facilitate an autonomous level prediction for one or more vehicles associated with the second geographic area. For instance, in one or more embodiments, the apparatus 102, such as the processing circuitry 106, can be configured to map the predicted autonomous transition data for the second geographic area onto a road network map associated with the second geographic region. In one or more embodiments, the apparatus 102, such as the processing circuitry 106, can be configured to generate and/or update transition pattern data (e.g., disengagement pattern data and/or engagement pattern data) for a map layer associated with the second geographic area based on the predicted autonomous transition data for the second geographic area. For example, in one or more embodiments, the apparatus 102, such as the processing circuitry 106, can be configured to generate and/or update transition patterns (e.g., disengagement patterns and/or engagement patterns) associated with historical data for the second geographic area. In one or more embodiments, the apparatus 102, such as the processing circuitry 106, can be configured to combine real-time transition data for the second geographic area with historical transition patterns (e.g., historical disengagement patterns and/or historical engagement patterns) for the second geographic area. In one or more embodiments, the predicted autonomous transition data for the second geographic area can be encoded in the database and/or can be employed by one or more vehicles associated with the second geographic area to facilitate autonomous driving for one or more vehicles when traveling within the second geographic area. In one or more embodiments, one or more notifications can be provided to a display of one or more vehicles associated with the second geographic area based on the predicted autonomous transition data for the second geographic area.” (Para 0092)
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the information processing device of Takemura with the method for predicting autonomous transition regions of Stenneth. One of ordinary skill in the art would have been motivated to make these modifications so that “precision and/or confidence of autonomous driving capabilities for a vehicle can be improved.” (Stenneth Para 0004)
Claim 5:
Takemura in combination with the references relied upon in Claim 4 teach those respective limitations. Takemura further teaches:
wherein the recognition action control unit performs control, [on a basis of the map information accumulated in the map information accumulation unit], to switch at least any of the recognition algorithm of the recognition unit and the action planning algorithm of the action planning unit to the recognition algorithm and the action planning algorithm that correspond to a new traveling environment at a position where the traveling environment corresponding to the position information changes.
(Takemura) – “First, constituent elements will be described by use of FIG. 1. As one embodiment for executing, by means of time sharing, the plurality of applications (hereinafter, may be described as "multi-applications") capable of dynamically switching the processing cycles of the application based on determination at the normal time or at the time of emergency in vehicle traveling, shown is a configuration of an image processing device (hereinafter, may be described as a "road-environment recognition image processing device") for executing environment recognition of a road by use of information from the on-vehicle camera.” (Para 0076)
“The road-environment recognition image processing device includes a processing section 100 for processing an image from a measurement device 101 such as the camera or the radar, an own-vehicle traveling information section 201, and a control/alarm determining section 300. The processing section 100 processes the information from a device such as the camera or the radar; detects a situation of the road; and make determination. For example, the processing section 100 receives from the measurement device the information for detecting and determining an obstacle including a human or an alternate vehicle in terms of traveling; a white line or a road shoulder, and further, a mark or a signal etc.; or a road state etc. such as a curve.” (Para 0077)
“This embodiment is an example in which the camera imaging section 101 is used as a representative. The processing section 100 is explained by a content in which image processing is executed by using the image imaged by the camera imaging section 101. However, with the same way of thinking, the processing section 100 is also applicable to the processing by use of other information. Based on the determination of the control/alarm determining section 300, a multi-application controlling section 111 determines to execute the processing cycles or operation of the respective functions (described as "ON"); to stop execution (described as "OFF"); or the like. Based on the result, a multi-application executing section 121 executes the respective functions. In the case where a recognition result by the on-vehicle camera is used to execute vehicle control such as collision avoidance or traffic-lane departure avoidance, it is desirable for a recognition processing cycle of the camera to be shorter, compared with the time of normal traveling. Contrarily, at the time of normal traveling having been prepared for the emergency as described above, it is not so much necessary to recognize an external world of the vehicle with the short processing cycle as at the time of emergency. The multi-application controlling section 111 controls the processing cycle or ON/OFF of execution operation, thereby enabling to execute the recognition processing by the camera concerning the collision avoidance or the traffic-lane departure avoidance at the time of emergency with the short processing cycle. Meanwhile, at the time of normal traveling of the vehicle, it is possible to operate the application which pursues the convenience by means of time sharing, and to utilize limited processing capacity of a processor more effectively.” (Para 0078)
“The own-vehicle traveling information section 201 transmits the information regarding the traveling of the own vehicle via an on-vehicle communication network to the processing section 100. The own-vehicle traveling information section 201 includes the vehicle-speed sensor 211, the rudder-angle sensor 212, a turning indicator 213, application operation 214, and the car navigation system 215, of the own vehicle.” (Para 0085)
“The switching of execution conditions of the application as described above is based on the switching of the traveling environments from the general-road traveling to the expressway traveling. The switching of the traveling environments in this way leads to the change of the applications of to be executed, and leads to the change of the priority or other execution conditions. Methods for detecting the switching of the traveling environments include, for example, the switching of a gear change, the change in vehicle speed, passing through a tollgate of a toll road such as the expressway, or the information from the car navigation system.” (Para 0091)
“In the case where in the lane recognition application 130 or the pedestrian detection application 140, the priority is placed on positional accuracy of the detection result, or the farther distance is the detection object, it is desirable to use the image having the high resolution. However, it is likely that calculation time will increase due to the use of the high-resolution image, and because of its processing load, an emphasis, such as not being in time for the processing cycle, will be given. The resolution is adjusted before executing the application in accordance with the necessary resolution in response to the conditions such as detection position accuracy, a detection object distance, the processing cycle. For example, in the case of assuming the traffic-lane departure avoidance, a lateral position of the lane with respect to the vehicle or a yaw angle requires the recognition accuracy in some degree, however, a curvature value in the distance, a lane position in the distance farther from a prediction position of the traffic-lane departure, or the like is not so much important. Under the circumstance where the vehicle is controlled for the traffic-lane departure avoidance, the image having the low resolution is used to reduce processing costs, whereby the update cycle of the lateral position of the lane or the yaw angle to be used for the vehicle control is hastened, and the higher-level vehicle control is actualized.” (Para 0096)
Examiner Note: Bracketed text not explicitly taught by primary reference, but is taught by non-primary reference later in the rejection. This notation will be used throughout the rejection unless otherwise noted.
Takemura does not explicitly teach:
on a basis of the map information accumulated in the map information accumulation unit
Stenneth, in the same field of endeavor of vehicle control, teaches:
on a basis of the map information accumulated in the map information accumulation unit
(Stenneth) – “With reference to FIG. 1, a system 100 configured to predict autonomous transition regions using historical information is depicted, in accordance with one or more embodiments of the present disclosure. In the illustrated embodiment, the system 100 includes an apparatus 102 and a map database 104. As described further below, the apparatus 102 is configured in accordance with an example embodiment of the present disclosure to assist navigation of a vehicle and/or to autonomous driving for a vehicle. The apparatus 102 can be embodied by any of a wide variety of computing devices including, for example, a computer system of a vehicle, a vehicle system of a vehicle, a navigation system of a vehicle, a control system of a vehicle, an electronic control unit of a vehicle, an autonomous vehicle control system (e.g., an autonomous-driving control system) of a vehicle, a mapping system of a vehicle, an Advanced Driver Assistance System module (ADAS of a vehicle), or any other type of computing device carried by or remote from the vehicle including, for example, a server or a distributed network of computing devices.” (Para 0047)
“In one or more embodiments, the apparatus 102, such as the processing circuitry 106, can be configured to generate one or more road links (e.g., one or more map-matched road links) for the predicted autonomous transition data for the second geographic area to facilitate an autonomous level prediction for one or more vehicles associated with the second geographic area. For instance, in one or more embodiments, the apparatus 102, such as the processing circuitry 106, can be configured to map the predicted autonomous transition data for the second geographic area onto a road network map associated with the second geographic region. In one or more embodiments, the apparatus 102, such as the processing circuitry 106, can be configured to generate and/or update transition pattern data (e.g., disengagement pattern data and/or engagement pattern data) for a map layer associated with the second geographic area based on the predicted autonomous transition data for the second geographic area. For example, in one or more embodiments, the apparatus 102, such as the processing circuitry 106, can be configured to generate and/or update transition patterns (e.g., disengagement patterns and/or engagement patterns) associated with historical data for the second geographic area. In one or more embodiments, the apparatus 102, such as the processing circuitry 106, can be configured to combine real-time transition data for the second geographic area with historical transition patterns (e.g., historical disengagement patterns and/or historical engagement patterns) for the second geographic area. In one or more embodiments, the predicted autonomous transition data for the second geographic area can be encoded in the database and/or can be employed by one or more vehicles associated with the second geographic area to facilitate autonomous driving for one or more vehicles when traveling within the second geographic area. In one or more embodiments, one or more notifications can be provided to a display of one or more vehicles associated with the second geographic area based on the predicted autonomous transition data for the second geographic area.” (Para 0092)
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the information processing device of Takemura with the method for predicting autonomous transition regions of Stenneth. One of ordinary skill in the art would have been motivated to make these modifications so that “precision and/or confidence of autonomous driving capabilities for a vehicle can be improved.” (Stenneth Para 0004)
Claim 6:
Takemura in combination with the references relied upon in Claim 5 teach those respective limitations. Takemura further teaches:
wherein the recognition action control unit performs control, [on a basis of the map information accumulated in the map information accumulation unit], to stop the mobile apparatus and switch at least any of the recognition algorithm of the recognition unit and the action planning algorithm of the action planning unit to the recognition algorithm and the action planning algorithm that correspond to the new traveling environment at the position where the traveling environment corresponding to the position information changes.
(Takemura) – “First, constituent elements will be described by use of FIG. 1. As one embodiment for executing, by means of time sharing, the plurality of applications (hereinafter, may be described as "multi-applications") capable of dynamically switching the processing cycles of the application based on determination at the normal time or at the time of emergency in vehicle traveling, shown is a configuration of an image processing device (hereinafter, may be described as a "road-environment recognition image processing device") for executing environment recognition of a road by use of information from the on-vehicle camera.” (Para 0076)
“The road-environment recognition image processing device includes a processing section 100 for processing an image from a measurement device 101 such as the camera or the radar, an own-vehicle traveling information section 201, and a control/alarm determining section 300. The processing section 100 processes the information from a device such as the camera or the radar; detects a situation of the road; and make determination. For example, the processing section 100 receives from the measurement device the information for detecting and determining an obstacle including a human or an alternate vehicle in terms of traveling; a white line or a road shoulder, and further, a mark or a signal etc.; or a road state etc. such as a curve.” (Para 0077)
“This embodiment is an example in which the camera imaging section 101 is used as a representative. The processing section 100 is explained by a content in which image processing is executed by using the image imaged by the camera imaging section 101. However, with the same way of thinking, the processing section 100 is also applicable to the processing by use of other information. Based on the determination of the control/alarm determining section 300, a multi-application controlling section 111 determines to execute the processing cycles or operation of the respective functions (described as "ON"); to stop execution (described as "OFF"); or the like. Based on the result, a multi-application executing section 121 executes the respective functions. In the case where a recognition result by the on-vehicle camera is used to execute vehicle control such as collision avoidance or traffic-lane departure avoidance, it is desirable for a recognition processing cycle of the camera to be shorter, compared with the time of normal traveling. Contrarily, at the time of normal traveling having been prepared for the emergency as described above, it is not so much necessary to recognize an external world of the vehicle with the short processing cycle as at the time of emergency. The multi-application controlling section 111 controls the processing cycle or ON/OFF of execution operation, thereby enabling to execute the recognition processing by the camera concerning the collision avoidance or the traffic-lane departure avoidance at the time of emergency with the short processing cycle. Meanwhile, at the time of normal traveling of the vehicle, it is possible to operate the application which pursues the convenience by means of time sharing, and to utilize limited processing capacity of a processor more effectively.” (Para 0078)
“The own-vehicle traveling information section 201 transmits the information regarding the traveling of the own vehicle via an on-vehicle communication network to the processing section 100. The own-vehicle traveling information section 201 includes the vehicle-speed sensor 211, the rudder-angle sensor 212, a turning indicator 213, application operation 214, and the car navigation system 215, of the own vehicle.” (Para 0085)
“The switching of execution conditions of the application as described above is based on the switching of the traveling environments from the general-road traveling to the expressway traveling. The switching of the traveling environments in this way leads to the change of the applications of to be executed, and leads to the change of the priority or other execution conditions. Methods for detecting the switching of the traveling environments include, for example, the switching of a gear change, the change in vehicle speed, passing through a tollgate of a toll road such as the expressway, or the information from the car navigation system.” (Para 0091)
“In the case where in the lane recognition application 130 or the pedestrian detection application 140, the priority is placed on positional accuracy of the detection result, or the farther distance is the detection object, it is desirable to use the image having the high resolution. However, it is likely that calculation time will increase due to the use of the high-resolution image, and because of its processing load, an emphasis, such as not being in time for the processing cycle, will be given. The resolution is adjusted before executing the application in accordance with the necessary resolution in response to the conditions such as detection position accuracy, a detection object distance, the processing cycle. For example, in the case of assuming the traffic-lane departure avoidance, a lateral position of the lane with respect to the vehicle or a yaw angle requires the recognition accuracy in some degree, however, a curvature value in the distance, a lane position in the distance farther from a prediction position of the traffic-lane departure, or the like is not so much important. Under the circumstance where the vehicle is controlled for the traffic-lane departure avoidance, the image having the low resolution is used to reduce processing costs, whereby the update cycle of the lateral position of the lane or the yaw angle to be used for the vehicle control is hastened, and the higher-level vehicle control is actualized.” (Para 0096)
Takemura does not explicitly teach:
on a basis of the map information accumulated in the map information accumulation unit
Stenneth, in the same field of endeavor of vehicle control, teaches:
on a basis of the map information accumulated in the map information accumulation unit
(Stenneth) – “With reference to FIG. 1, a system 100 configured to predict autonomous transition regions using historical information is depicted, in accordance with one or more embodiments of the present disclosure. In the illustrated embodiment, the system 100 includes an apparatus 102 and a map database 104. As described further below, the apparatus 102 is configured in accordance with an example embodiment of the present disclosure to assist navigation of a vehicle and/or to autonomous driving for a vehicle. The apparatus 102 can be embodied by any of a wide variety of computing devices including, for example, a computer system of a vehicle, a vehicle system of a vehicle, a navigation system of a vehicle, a control system of a vehicle, an electronic control unit of a vehicle, an autonomous vehicle control system (e.g., an autonomous-driving control system) of a vehicle, a mapping system of a vehicle, an Advanced Driver Assistance System module (ADAS of a vehicle), or any other type of computing device carried by or remote from the vehicle including, for example, a server or a distributed network of computing devices.” (Para 0047)
“In one or more embodiments, the apparatus 102, such as the processing circuitry 106, can be configured to generate one or more road links (e.g., one or more map-matched road links) for the predicted autonomous transition data for the second geographic area to facilitate an autonomous level prediction for one or more vehicles associated with the second geographic area. For instance, in one or more embodiments, the apparatus 102, such as the processing circuitry 106, can be configured to map the predicted autonomous transition data for the second geographic area onto a road network map associated with the second geographic region. In one or more embodiments, the apparatus 102, such as the processing circuitry 106, can be configured to generate and/or update transition pattern data (e.g., disengagement pattern data and/or engagement pattern data) for a map layer associated with the second geographic area based on the predicted autonomous transition data for the second geographic area. For example, in one or more embodiments, the apparatus 102, such as the processing circuitry 106, can be configured to generate and/or update transition patterns (e.g., disengagement patterns and/or engagement patterns) associated with historical data for the second geographic area. In one or more embodiments, the apparatus 102, such as the processing circuitry 106, can be configured to combine real-time transition data for the second geographic area with historical transition patterns (e.g., historical disengagement patterns and/or historical engagement patterns) for the second geographic area. In one or more embodiments, the predicted autonomous transition data for the second geographic area can be encoded in the database and/or can be employed by one or more vehicles associated with the second geographic area to facilitate autonomous driving for one or more vehicles when traveling within the second geographic area. In one or more embodiments, one or more notifications can be provided to a display of one or more vehicles associated with the second geographic area based on the predicted autonomous transition data for the second geographic area.” (Para 0092)
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the information processing device of Takemura with the method for predicting autonomous transition regions of Stenneth. One of ordinary skill in the art would have been motivated to make these modifications so that “precision and/or confidence of autonomous driving capabilities for a vehicle can be improved.” (Stenneth Para 0004)
Claim 7:
Takemura in combination with the references relied upon in Claim 5 teach those respective limitations. Takemura further teaches:
the recognition action control unit controls to switch at least any of the recognition algorithm of the recognition unit and the action planning algorithm of the action planning unit to the recognition algorithm and the action planning algorithm that correspond to the new traveling environment while moving the mobile apparatus [when the mobile apparatus is within the switching zone].
(Takemura) – “First, constituent elements will be described by use of FIG. 1. As one embodiment for executing, by means of time sharing, the plurality of applications (hereinafter, may be described as "multi-applications") capable of dynamically switching the processing cycles of the application based on determination at the normal time or at the time of emergency in vehicle traveling, shown is a configuration of an image processing device (hereinafter, may be described as a "road-environment recognition image processing device") for executing environment recognition of a road by use of information from the on-vehicle camera.” (Para 0076)
“The road-environment recognition image processing device includes a processing section 100 for processing an image from a measurement device 101 such as the camera or the radar, an own-vehicle traveling information section 201, and a control/alarm determining section 300. The processing section 100 processes the information from a device such as the camera or the radar; detects a situation of the road; and make determination. For example, the processing section 100 receives from the measurement device the information for detecting and determining an obstacle including a human or an alternate vehicle in terms of traveling; a white line or a road shoulder, and further, a mark or a signal etc.; or a road state etc. such as a curve.” (Para 0077)
“This embodiment is an example in which the camera imaging section 101 is used as a representative. The processing section 100 is explained by a content in which image processing is executed by using the image imaged by the camera imaging section 101. However, with the same way of thinking, the processing section 100 is also applicable to the processing by use of other information. Based on the determination of the control/alarm determining section 300, a multi-application controlling section 111 determines to execute the processing cycles or operation of the respective functions (described as "ON"); to stop execution (described as "OFF"); or the like. Based on the result, a multi-application executing section 121 executes the respective functions. In the case where a recognition result by the on-vehicle camera is used to execute vehicle control such as collision avoidance or traffic-lane departure avoidance, it is desirable for a recognition processing cycle of the camera to be shorter, compared with the time of normal traveling. Contrarily, at the time of normal traveling having been prepared for the emergency as described above, it is not so much necessary to recognize an external world of the vehicle with the short processing cycle as at the time of emergency. The multi-application controlling section 111 controls the processing cycle or ON/OFF of execution operation, thereby enabling to execute the recognition processing by the camera concerning the collision avoidance or the traffic-lane departure avoidance at the time of emergency with the short processing cycle. Meanwhile, at the time of normal traveling of the vehicle, it is possible to operate the application which pursues the convenience by means of time sharing, and to utilize limited processing capacity of a processor more effectively.” (Para 0078)
“The own-vehicle traveling information section 201 transmits the information regarding the traveling of the own vehicle via an on-vehicle communication network to the processing section 100. The own-vehicle traveling information section 201 includes the vehicle-speed sensor 211, the rudder-angle sensor 212, a turning indicator 213, application operation 214, and the car navigation system 215, of the own vehicle.” (Para 0085)
“The switching of execution conditions of the application as described above is based on the switching of the traveling environments from the general-road traveling to the expressway traveling. The switching of the traveling environments in this way leads to the change of the applications of to be executed, and leads to the change of the priority or other execution conditions. Methods for detecting the switching of the traveling environments include, for example, the switching of a gear change, the change in vehicle speed, passing through a tollgate of a toll road such as the expressway, or the information from the car navigation system.” (Para 0091)
“In the case where in the lane recognition application 130 or the pedestrian detection application 140, the priority is placed on positional accuracy of the detection result, or the farther distance is the detection object, it is desirable to use the image having the high resolution. However, it is likely that calculation time will increase due to the use of the high-resolution image, and because of its processing load, an emphasis, such as not being in time for the processing cycle, will be given. The resolution is adjusted before executing the application in accordance with the necessary resolution in response to the conditions such as detection position accuracy, a detection object distance, the processing cycle. For example, in the case of assuming the traffic-lane departure avoidance, a lateral position of the lane with respect to the vehicle or a yaw angle requires the recognition accuracy in some degree, however, a curvature value in the distance, a lane position in the distance farther from a prediction position of the traffic-lane departure, or the like is not so much important. Under the circumstance where the vehicle is controlled for the traffic-lane departure avoidance, the image having the low resolution is used to reduce processing costs, whereby the update cycle of the lateral position of the lane or the yaw angle to be used for the vehicle control is hastened, and the higher-level vehicle control is actualized.” (Para 0096)
Takemura does not explicitly teach:
wherein a switching zone is set at a position a predetermined distance before the position where the traveling environment changes, and…when the mobile apparatus is within the switching zone
Stenneth, in the same field of endeavor of vehicle control, teaches:
wherein a switching zone is set at a position a predetermined distance before the position where the traveling environment changes, and…when the mobile apparatus is within the switching zone
(Stenneth) – “According to one or more embodiments, autonomous transition regions for a geographic can be predicted using historical information from one or more other geographic areas. For instance, in one or more embodiments, historical data related to autonomous transition regions in a first area (e.g., a first city) can be determined. Furthermore, in a second area (e.g., a second city) where autonomous vehicles have not yet been deployed, autonomous transition patterns associated with the first area (e.g., the first city) can be employed to predict which areas in the second area (e.g., the second city) will become autonomous transition regions for autonomous vehicles. In one or more embodiments, for each autonomous transition region in the first area, information related to one or more autonomous level transition reasons (e.g., a reason why autonomous vehicles changed respective autonomous levels) can be determined. The autonomous level transition reason can be a reason for a vehicle to transition to a different level of defined autonomy. In an embodiment, the autonomous level transition reason can be a reason that a vehicle disengages from a particular level of defined autonomy. In another embodiment, the autonomous level transition reason can be a reason that a vehicle engages into a particular level of defined autonomy. The autonomous level transition reason can include, for example, a decrease in communication signal strength for a vehicle (e.g. loss of a 5G signal employed by a vehicle), a road condition (e.g., road construction, high pedestrian traffic, etc.) being present at a road segment associated with a vehicle, a particular environmental condition (e.g., a particular weather condition, etc.) present at a road segment associated with a vehicle, reduced visibility distance for a vehicle due to a particular environmental condition, sensor limitations of a vehicle, another type of reason, etc. In one or more embodiments, a training data set can be determined based on the historical data related to autonomous transition regions in the first area. In one or more embodiments, a feature in the training dataset can be associated with a reason for an autonomous level being changed for a vehicle. In one or more embodiments, the training data set can additionally include historical information for other regions in the first area that are not autonomous transition regions. In certain embodiments, a designer of an autonomous vehicle infrastructure can employ the prediction to provide informed decisions regarding autonomous driving in the second area such as, for example, to determine where to allow autonomous transition regions in the second area and/or to determine where to avoid autonomous transition regions in the second area.” (Para 0045)
“FIG. 3 illustrates a map 300 divided into autonomous transition regions. For example, the map 300 includes at least an autonomous transition region 302. In one or more embodiments, the autonomous transition region 302 can correspond to a spatial reference point. In an embodiment, the autonomous transition region 302 can be a tile cell or a grid cell. In a non-limiting example, the autonomous transition region 302 can be a 2 kilometer by 2 kilometer tile cell. However, it is to be appreciated that the autonomous transition region 302 can be a different shape and/or a different size.” (Para 0058)
“In one or more embodiments, the apparatus 102, such as the processing circuitry 106, can be configured to generate one or more road links (e.g., one or more map-matched road links) for the predicted autonomous transition data for the second geographic area to facilitate an autonomous level prediction for one or more vehicles associated with the second geographic area. For instance, in one or more embodiments, the apparatus 102, such as the processing circuitry 106, can be configured to map the predicted autonomous transition data for the second geographic area onto a road network map associated with the second geographic region. In one or more embodiments, the apparatus 102, such as the processing circuitry 106, can be configured to generate and/or update transition pattern data (e.g., disengagement pattern data and/or engagement pattern data) for a map layer associated with the second geographic area based on the predicted autonomous transition data for the second geographic area. For example, in one or more embodiments, the apparatus 102, such as the processing circuitry 106, can be configured to generate and/or update transition patterns (e.g., disengagement patterns and/or engagement patterns) associated with historical data for the second geographic area. In one or more embodiments, the apparatus 102, such as the processing circuitry 106, can be configured to combine real-time transition data for the second geographic area with historical transition patterns (e.g., historical disengagement patterns and/or historical engagement patterns) for the second geographic area. In one or more embodiments, the predicted autonomous transition data for the second geographic area can be encoded in the database and/or can be employed by one or more vehicles associated with the second geographic area to facilitate autonomous driving for one or more vehicles when traveling within the second geographic area. In one or more embodiments, one or more notifications can be provided to a display of one or more vehicles associated with the second geographic area based on the predicted autonomous transition data for the second geographic area.” (Para 0092)
“As illustrated in FIG. 9, the architecture includes the map data service provider 808 that provides map data 925 (e.g., HD maps and policies associated with road links within the map) to an Advanced Driver Assistance System (ADAS) 905, which may be vehicle-based or server based depending upon the application. The map data service provider 808 may be a cloud-based 910 service. In one or more embodiments, the ADAS 905 receives the autonomous transition data 902 and may provide autonomous transition data 902 to map matcher 915. The map matcher 915 may correlate the autonomous transition data 902 to a road link on a map of the mapped network of roads stored in the map cache 920. This link or segment, along with the direction of travel, may be used to establish which HD map policies are applicable to the vehicle associated with the ADAS 905, including sensor capability information, autonomous functionality information, etc. Accordingly, in one or more embodiments, policies for one or more vehicles are established based on the autonomous transition data 902. The map data 925 associated with the road segment specific to the vehicle are provided to the vehicle control, such as via the CAN (computer area network) BUS (or Ethernet or Flexray) 940 to the electronic control unit (ECU) 945 of the vehicle to implement HD map policies, such as various forms of autonomous or assisted driving, or navigation assistance. In certain embodiments, a data access layer 935 can manage and/or facilitate access to the map cache 920, the map data 925, and/or an ADAS map database 930. In an embodiment, at least a portion of the ADAS map database 930 can correspond to the map database 104 and/or the map database 810.” (Para 0095)
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the information processing device of Takemura with the method for predicting autonomous transition regions of Stenneth. One of ordinary skill in the art would have been motivated to make these modifications so that “precision and/or confidence of autonomous driving capabilities for a vehicle can be improved.” (Stenneth Para 0004)
Claim 8:
Takemura in combination with the references relied upon in Claim 7 teach those respective limitations. Takemura further teaches:
wherein the recognition action control unit controls to switch at least any of the recognition algorithm of the recognition unit and the action planning algorithm of the action planning unit to the recognition algorithm and the action planning algorithm that correspond to the new traveling environment while moving the mobile apparatus [by causing an external server to substitute for a function of at least any of the recognition unit and the action planning unit when the mobile apparatus is in the switching zone].
(Takemura) – “First, constituent elements will be described by use of FIG. 1. As one embodiment for executing, by means of time sharing, the plurality of applications (hereinafter, may be described as "multi-applications") capable of dynamically switching the processing cycles of the application based on determination at the normal time or at the time of emergency in vehicle traveling, shown is a configuration of an image processing device (hereinafter, may be described as a "road-environment recognition image processing device") for executing environment recognition of a road by use of information from the on-vehicle camera.” (Para 0076)
“The road-environment recognition image processing device includes a processing section 100 for processing an image from a measurement device 101 such as the camera or the radar, an own-vehicle traveling information section 201, and a control/alarm determining section 300. The processing section 100 processes the information from a device such as the camera or the radar; detects a situation of the road; and make determination. For example, the processing section 100 receives from the measurement device the information for detecting and determining an obstacle including a human or an alternate vehicle in terms of traveling; a white line or a road shoulder, and further, a mark or a signal etc.; or a road state etc. such as a curve.” (Para 0077)
“This embodiment is an example in which the camera imaging section 101 is used as a representative. The processing section 100 is explained by a content in which image processing is executed by using the image imaged by the camera imaging section 101. However, with the same way of thinking, the processing section 100 is also applicable to the processing by use of other information. Based on the determination of the control/alarm determining section 300, a multi-application controlling section 111 determines to execute the processing cycles or operation of the respective functions (described as "ON"); to stop execution (described as "OFF"); or the like. Based on the result, a multi-application executing section 121 executes the respective functions. In the case where a recognition result by the on-vehicle camera is used to execute vehicle control such as collision avoidance or traffic-lane departure avoidance, it is desirable for a recognition processing cycle of the camera to be shorter, compared with the time of normal traveling. Contrarily, at the time of normal traveling having been prepared for the emergency as described above, it is not so much necessary to recognize an external world of the vehicle with the short processing cycle as at the time of emergency. The multi-application controlling section 111 controls the processing cycle or ON/OFF of execution operation, thereby enabling to execute the recognition processing by the camera concerning the collision avoidance or the traffic-lane departure avoidance at the time of emergency with the short processing cycle. Meanwhile, at the time of normal traveling of the vehicle, it is possible to operate the application which pursues the convenience by means of time sharing, and to utilize limited processing capacity of a processor more effectively.” (Para 0078)
“The own-vehicle traveling information section 201 transmits the information regarding the traveling of the own vehicle via an on-vehicle communication network to the processing section 100. The own-vehicle traveling information section 201 includes the vehicle-speed sensor 211, the rudder-angle sensor 212, a turning indicator 213, application operation 214, and the car navigation system 215, of the own vehicle.” (Para 0085)
“The switching of execution conditions of the application as described above is based on the switching of the traveling environments from the general-road traveling to the expressway traveling. The switching of the traveling environments in this way leads to the change of the applications of to be executed, and leads to the change of the priority or other execution conditions. Methods for detecting the switching of the traveling environments include, for example, the switching of a gear change, the change in vehicle speed, passing through a tollgate of a toll road such as the expressway, or the information from the car navigation system.” (Para 0091)
“In the case where in the lane recognition application 130 or the pedestrian detection application 140, the priority is placed on positional accuracy of the detection result, or the farther distance is the detection object, it is desirable to use the image having the high resolution. However, it is likely that calculation time will increase due to the use of the high-resolution image, and because of its processing load, an emphasis, such as not being in time for the processing cycle, will be given. The resolution is adjusted before executing the application in accordance with the necessary resolution in response to the conditions such as detection position accuracy, a detection object distance, the processing cycle. For example, in the case of assuming the traffic-lane departure avoidance, a lateral position of the lane with respect to the vehicle or a yaw angle requires the recognition accuracy in some degree, however, a curvature value in the distance, a lane position in the distance farther from a prediction position of the traffic-lane departure, or the like is not so much important. Under the circumstance where the vehicle is controlled for the traffic-lane departure avoidance, the image having the low resolution is used to reduce processing costs, whereby the update cycle of the lateral position of the lane or the yaw angle to be used for the vehicle control is hastened, and the higher-level vehicle control is actualized.” (Para 0096)
Takemura does not explicitly teach:
by causing an external server to substitute for a function of at least any of the recognition unit and the action planning unit when the mobile apparatus is in the switching zone
Stenneth, in the same field of endeavor of vehicle control, teaches:
by causing an external server to substitute for a function of at least any of the recognition unit and the action planning unit when the mobile apparatus is in the switching zone
(Stenneth) – “According to one or more embodiments, autonomous transition regions for a geographic can be predicted using historical information from one or more other geographic areas. For instance, in one or more embodiments, historical data related to autonomous transition regions in a first area (e.g., a first city) can be determined. Furthermore, in a second area (e.g., a second city) where autonomous vehicles have not yet been deployed, autonomous transition patterns associated with the first area (e.g., the first city) can be employed to predict which areas in the second area (e.g., the second city) will become autonomous transition regions for autonomous vehicles. In one or more embodiments, for each autonomous transition region in the first area, information related to one or more autonomous level transition reasons (e.g., a reason why autonomous vehicles changed respective autonomous levels) can be determined. The autonomous level transition reason can be a reason for a vehicle to transition to a different level of defined autonomy. In an embodiment, the autonomous level transition reason can be a reason that a vehicle disengages from a particular level of defined autonomy. In another embodiment, the autonomous level transition reason can be a reason that a vehicle engages into a particular level of defined autonomy. The autonomous level transition reason can include, for example, a decrease in communication signal strength for a vehicle (e.g. loss of a 5G signal employed by a vehicle), a road condition (e.g., road construction, high pedestrian traffic, etc.) being present at a road segment associated with a vehicle, a particular environmental condition (e.g., a particular weather condition, etc.) present at a road segment associated with a vehicle, reduced visibility distance for a vehicle due to a particular environmental condition, sensor limitations of a vehicle, another type of reason, etc. In one or more embodiments, a training data set can be determined based on the historical data related to autonomous transition regions in the first area. In one or more embodiments, a feature in the training dataset can be associated with a reason for an autonomous level being changed for a vehicle. In one or more embodiments, the training data set can additionally include historical information for other regions in the first area that are not autonomous transition regions. In certain embodiments, a designer of an autonomous vehicle infrastructure can employ the prediction to provide informed decisions regarding autonomous driving in the second area such as, for example, to determine where to allow autonomous transition regions in the second area and/or to determine where to avoid autonomous transition regions in the second area.” (Para 0045)
“FIG. 3 illustrates a map 300 divided into autonomous transition regions. For example, the map 300 includes at least an autonomous transition region 302. In one or more embodiments, the autonomous transition region 302 can correspond to a spatial reference point. In an embodiment, the autonomous transition region 302 can be a tile cell or a grid cell. In a non-limiting example, the autonomous transition region 302 can be a 2 kilometer by 2 kilometer tile cell. However, it is to be appreciated that the autonomous transition region 302 can be a different shape and/or a different size.” (Para 0058)
“In one or more embodiments, the apparatus 102, such as the processing circuitry 106, can be configured to generate one or more road links (e.g., one or more map-matched road links) for the predicted autonomous transition data for the second geographic area to facilitate an autonomous level prediction for one or more vehicles associated with the second geographic area. For instance, in one or more embodiments, the apparatus 102, such as the processing circuitry 106, can be configured to map the predicted autonomous transition data for the second geographic area onto a road network map associated with the second geographic region. In one or more embodiments, the apparatus 102, such as the processing circuitry 106, can be configured to generate and/or update transition pattern data (e.g., disengagement pattern data and/or engagement pattern data) for a map layer associated with the second geographic area based on the predicted autonomous transition data for the second geographic area. For example, in one or more embodiments, the apparatus 102, such as the processing circuitry 106, can be configured to generate and/or update transition patterns (e.g., disengagement patterns and/or engagement patterns) associated with historical data for the second geographic area. In one or more embodiments, the apparatus 102, such as the processing circuitry 106, can be configured to combine real-time transition data for the second geographic area with historical transition patterns (e.g., historical disengagement patterns and/or historical engagement patterns) for the second geographic area. In one or more embodiments, the predicted autonomous transition data for the second geographic area can be encoded in the database and/or can be employed by one or more vehicles associated with the second geographic area to facilitate autonomous driving for one or more vehicles when traveling within the second geographic area. In one or more embodiments, one or more notifications can be provided to a display of one or more vehicles associated with the second geographic area based on the predicted autonomous transition data for the second geographic area.” (Para 0092)
“As illustrated in FIG. 9, the architecture includes the map data service provider 808 that provides map data 925 (e.g., HD maps and policies associated with road links within the map) to an Advanced Driver Assistance System (ADAS) 905, which may be vehicle-based or server based depending upon the application. The map data service provider 808 may be a cloud-based 910 service. In one or more embodiments, the ADAS 905 receives the autonomous transition data 902 and may provide autonomous transition data 902 to map matcher 915. The map matcher 915 may correlate the autonomous transition data 902 to a road link on a map of the mapped network of roads stored in the map cache 920. This link or segment, along with the direction of travel, may be used to establish which HD map policies are applicable to the vehicle associated with the ADAS 905, including sensor capability information, autonomous functionality information, etc. Accordingly, in one or more embodiments, policies for one or more vehicles are established based on the autonomous transition data 902. The map data 925 associated with the road segment specific to the vehicle are provided to the vehicle control, such as via the CAN (computer area network) BUS (or Ethernet or Flexray) 940 to the electronic control unit (ECU) 945 of the vehicle to implement HD map policies, such as various forms of autonomous or assisted driving, or navigation assistance. In certain embodiments, a data access layer 935 can manage and/or facilitate access to the map cache 920, the map data 925, and/or an ADAS map database 930. In an embodiment, at least a portion of the ADAS map database 930 can correspond to the map database 104 and/or the map database 810.” (Para 0095)
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the information processing device of Takemura with the method for predicting autonomous transition regions of Stenneth. One of ordinary skill in the art would have been motivated to make these modifications so that “precision and/or confidence of autonomous driving capabilities for a vehicle can be improved.” (Stenneth Para 0004)
Claim 9:
Takemura in combination with the references relied upon in Claim 8 teach those respective limitations. Takemura does not explicitly teach the following limitations in full. Stenneth further teaches:
wherein the external server is a cloud computer that is capable of substituting for functions of the recognition unit and the action planning unit.
(Stenneth) – “According to one or more embodiments, autonomous transition regions for a geographic can be predicted using historical information from one or more other geographic areas. For instance, in one or more embodiments, historical data related to autonomous transition regions in a first area (e.g., a first city) can be determined. Furthermore, in a second area (e.g., a second city) where autonomous vehicles have not yet been deployed, autonomous transition patterns associated with the first area (e.g., the first city) can be employed to predict which areas in the second area (e.g., the second city) will become autonomous transition regions for autonomous vehicles. In one or more embodiments, for each autonomous transition region in the first area, information related to one or more autonomous level transition reasons (e.g., a reason why autonomous vehicles changed respective autonomous levels) can be determined. The autonomous level transition reason can be a reason for a vehicle to transition to a different level of defined autonomy. In an embodiment, the autonomous level transition reason can be a reason that a vehicle disengages from a particular level of defined autonomy. In another embodiment, the autonomous level transition reason can be a reason that a vehicle engages into a particular level of defined autonomy. The autonomous level transition reason can include, for example, a decrease in communication signal strength for a vehicle (e.g. loss of a 5G signal employed by a vehicle), a road condition (e.g., road construction, high pedestrian traffic, etc.) being present at a road segment associated with a vehicle, a particular environmental condition (e.g., a particular weather condition, etc.) present at a road segment associated with a vehicle, reduced visibility distance for a vehicle due to a particular environmental condition, sensor limitations of a vehicle, another type of reason, etc. In one or more embodiments, a training data set can be determined based on the historical data related to autonomous transition regions in the first area. In one or more embodiments, a feature in the training dataset can be associated with a reason for an autonomous level being changed for a vehicle. In one or more embodiments, the training data set can additionally include historical information for other regions in the first area that are not autonomous transition regions. In certain embodiments, a designer of an autonomous vehicle infrastructure can employ the prediction to provide informed decisions regarding autonomous driving in the second area such as, for example, to determine where to allow autonomous transition regions in the second area and/or to determine where to avoid autonomous transition regions in the second area.” (Para 0045)
“FIG. 3 illustrates a map 300 divided into autonomous transition regions. For example, the map 300 includes at least an autonomous transition region 302. In one or more embodiments, the autonomous transition region 302 can correspond to a spatial reference point. In an embodiment, the autonomous transition region 302 can be a tile cell or a grid cell. In a non-limiting example, the autonomous transition region 302 can be a 2 kilometer by 2 kilometer tile cell. However, it is to be appreciated that the autonomous transition region 302 can be a different shape and/or a different size.” (Para 0058)
“In one or more embodiments, the apparatus 102, such as the processing circuitry 106, can be configured to generate one or more road links (e.g., one or more map-matched road links) for the predicted autonomous transition data for the second geographic area to facilitate an autonomous level prediction for one or more vehicles associated with the second geographic area. For instance, in one or more embodiments, the apparatus 102, such as the processing circuitry 106, can be configured to map the predicted autonomous transition data for the second geographic area onto a road network map associated with the second geographic region. In one or more embodiments, the apparatus 102, such as the processing circuitry 106, can be configured to generate and/or update transition pattern data (e.g., disengagement pattern data and/or engagement pattern data) for a map layer associated with the second geographic area based on the predicted autonomous transition data for the second geographic area. For example, in one or more embodiments, the apparatus 102, such as the processing circuitry 106, can be configured to generate and/or update transition patterns (e.g., disengagement patterns and/or engagement patterns) associated with historical data for the second geographic area. In one or more embodiments, the apparatus 102, such as the processing circuitry 106, can be configured to combine real-time transition data for the second geographic area with historical transition patterns (e.g., historical disengagement patterns and/or historical engagement patterns) for the second geographic area. In one or more embodiments, the predicted autonomous transition data for the second geographic area can be encoded in the database and/or can be employed by one or more vehicles associated with the second geographic area to facilitate autonomous driving for one or more vehicles when traveling within the second geographic area. In one or more embodiments, one or more notifications can be provided to a display of one or more vehicles associated with the second geographic area based on the predicted autonomous transition data for the second geographic area.” (Para 0092)
“As illustrated in FIG. 9, the architecture includes the map data service provider 808 that provides map data 925 (e.g., HD maps and policies associated with road links within the map) to an Advanced Driver Assistance System (ADAS) 905, which may be vehicle-based or server based depending upon the application. The map data service provider 808 may be a cloud-based 910 service. In one or more embodiments, the ADAS 905 receives the autonomous transition data 902 and may provide autonomous transition data 902 to map matcher 915. The map matcher 915 may correlate the autonomous transition data 902 to a road link on a map of the mapped network of roads stored in the map cache 920. This link or segment, along with the direction of travel, may be used to establish which HD map policies are applicable to the vehicle associated with the ADAS 905, including sensor capability information, autonomous functionality information, etc. Accordingly, in one or more embodiments, policies for one or more vehicles are established based on the autonomous transition data 902. The map data 925 associated with the road segment specific to the vehicle are provided to the vehicle control, such as via the CAN (computer area network) BUS (or Ethernet or Flexray) 940 to the electronic control unit (ECU) 945 of the vehicle to implement HD map policies, such as various forms of autonomous or assisted driving, or navigation assistance. In certain embodiments, a data access layer 935 can manage and/or facilitate access to the map cache 920, the map data 925, and/or an ADAS map database 930. In an embodiment, at least a portion of the ADAS map database 930 can correspond to the map database 104 and/or the map database 810.” (Para 0095)
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the information processing device of Takemura with the method for predicting autonomous transition regions of Stenneth. One of ordinary skill in the art would have been motivated to make these modifications so that “precision and/or confidence of autonomous driving capabilities for a vehicle can be improved.” (Stenneth Para 0004)
Claim(s) 13-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Takemura (US20110205042) in view of Gao (“Deep Reinforcement Learning for Indoor Mobile Robot Path Planning”).
Claim 13:
Takemura teaches the respective limitations of Claim 1. Takemura does not explicitly teach the following limitations in whole. Gao, in the same field of endeavor of vehicle control, teaches:
wherein the action planning algorithm includes a dynamic window approach (DWA) method, a reinforcement learning method, and a local trajectory planner (LTP) method as algorithms for planning the travel route according to the traveling environment.
(Gao) – “This paper proposes a novel incremental training mode to address the problem of Deep
Reinforcement Learning (DRL) based path planning for a mobile robot. Firstly, we evaluate the
related graphic search algorithms and Reinforcement Learning (RL) algorithms in a lightweight
2D environment. Then, we design the algorithm based on DRL, including observation states,
reward function, network structure as well as parameters optimization, in a 2D environment to
circumvent the time-consuming works for a 3D environment.” (Abstract)
“A mobile robot with autonomous navigation capability can search for one path within specific
time to move from a start point to an endpoint without collision [1]. For indoor environments,
traditional navigation frameworks employ Simultaneous Localization and Mapping (SLAM) [2] to
map unknown environments, then exploit localization algorithms, for instance, Adaptive Monte Carlo Localization (AMCL) [3] to determine the current location of a robot and move it to the destination through a path planning module [4,5]. Path planning is one of the key technologies in navigation system, which usually includes the global as well as local planners [6,7]. The global planner searches the global map to obtain a feasible path by means of planning algorithms, such as Dijkstra, A-star (A*), PRM, Rapidly Exploring Random Tree (RRT) algorithms, and so on [8–12]. The Dijkstra is a classic algorithm for finding the shortest path between two points due to its optimal capability for global planning. The A* achieves better performance by using heuristics to guide its search, which can be seen as an extension of Dijkstra. A motion planning algorithm sampling-based, PRM, solves the problem of determining a path between a starting configuration and a goal configuration while avoiding collisions. The RRT is an algorithm designed to search non-convex, high-dimensional spaces by randomly building a space-filling tree. However, the local planner is used for real-time collision-free based on the global path by means of planning algorithms, such as Artificial Potential Field (APF), Dynamic Window
Approach (DWA) and Timed Elastic Bands (TEB) [13–15]. The APF treats a robot’s configuration as a point in a potential field that combines attraction to the goal, and repulsion from obstacles to track the safe path. One velocity-based local planner, DWA, calculates the optimal collision-free velocity for a robot required to reach its goal, which translates a Cartesian goal (x,y) into a velocity (v,w) command. The TEB merges the states, control inputs and time intervals into a joint trajectory to enable the path planning of time-optimal trajectories. Most of the global path planning algorithms are suitable for path-finding of static scenes and require prior map information. With the increase of complexity and uncertainty of the environment, their efficiency will be greatly reduced. The local path planning obtains the surrounding environment information through sensors, and generates a collision-free path in a related small scope with better adaptability and embarrassed local minimum. In conclusion, the traditional path planning suffers from the issues of map-dependence, low real-time performance, or expert experience-dependence [16].” (Pg 1-2 Sec 1)
“In recent years, there is a growing trend to apply DRL for mobile robot indoor path planning with much success achieved [17]. DRL has the advantages on map-free, strong learning ability and low sensor accuracy dependence, etc., whereas the prohibitively long training duration for DRL-based path planning has severely hindered its wide application to mobile robots, especially for the scenario of limited computation resources.” (Pg 2 Sec 1)
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the information processing device of Takemura with the method for path planning of Gao. One of ordinary skill in the art would have been motivated to make these modifications because “Experimental results show that the incremental training mode can notably improve the development efficiency” (Gao Abstract)
Claim 14:
Takemura in combination with the references relied upon in Claim 13 teach those respective limitations. Takemura further teaches:
wherein the recognition action control unit switches the action planning algorithm of the action planning unit [to the DWA method when it is necessary to reduce a load on the action planning unit in accordance with the traveling environment].
(Takemura) – “First, constituent elements will be described by use of FIG. 1. As one embodiment for executing, by means of time sharing, the plurality of applications (hereinafter, may be described as "multi-applications") capable of dynamically switching the processing cycles of the application based on determination at the normal time or at the time of emergency in vehicle traveling, shown is a configuration of an image processing device (hereinafter, may be described as a "road-environment recognition image processing device") for executing environment recognition of a road by use of information from the on-vehicle camera.” (Para 0076)
“The road-environment recognition image processing device includes a processing section 100 for processing an image from a measurement device 101 such as the camera or the radar, an own-vehicle traveling information section 201, and a control/alarm determining section 300. The processing section 100 processes the information from a device such as the camera or the radar; detects a situation of the road; and make determination. For example, the processing section 100 receives from the measurement device the information for detecting and determining an obstacle including a human or an alternate vehicle in terms of traveling; a white line or a road shoulder, and further, a mark or a signal etc.; or a road state etc. such as a curve.” (Para 0077)
“This embodiment is an example in which the camera imaging section 101 is used as a representative. The processing section 100 is explained by a content in which image processing is executed by using the image imaged by the camera imaging section 101. However, with the same way of thinking, the processing section 100 is also applicable to the processing by use of other information. Based on the determination of the control/alarm determining section 300, a multi-application controlling section 111 determines to execute the processing cycles or operation of the respective functions (described as "ON"); to stop execution (described as "OFF"); or the like. Based on the result, a multi-application executing section 121 executes the respective functions. In the case where a recognition result by the on-vehicle camera is used to execute vehicle control such as collision avoidance or traffic-lane departure avoidance, it is desirable for a recognition processing cycle of the camera to be shorter, compared with the time of normal traveling. Contrarily, at the time of normal traveling having been prepared for the emergency as described above, it is not so much necessary to recognize an external world of the vehicle with the short processing cycle as at the time of emergency. The multi-application controlling section 111 controls the processing cycle or ON/OFF of execution operation, thereby enabling to execute the recognition processing by the camera concerning the collision avoidance or the traffic-lane departure avoidance at the time of emergency with the short processing cycle. Meanwhile, at the time of normal traveling of the vehicle, it is possible to operate the application which pursues the convenience by means of time sharing, and to utilize limited processing capacity of a processor more effectively.” (Para 0078)
“The own-vehicle traveling information section 201 transmits the information regarding the traveling of the own vehicle via an on-vehicle communication network to the processing section 100. The own-vehicle traveling information section 201 includes the vehicle-speed sensor 211, the rudder-angle sensor 212, a turning indicator 213, application operation 214, and the car navigation system 215, of the own vehicle.” (Para 0085)
“The switching of execution conditions of the application as described above is based on the switching of the traveling environments from the general-road traveling to the expressway traveling. The switching of the traveling environments in this way leads to the change of the applications of to be executed, and leads to the change of the priority or other execution conditions. Methods for detecting the switching of the traveling environments include, for example, the switching of a gear change, the change in vehicle speed, passing through a tollgate of a toll road such as the expressway, or the information from the car navigation system.” (Para 0091)
Takemura does not explicitly teach:
to the DWA method when it is necessary to reduce a load on the action planning unit in accordance with the traveling environment
Gao, in the same field of endeavor of vehicle control, teaches:
to the DWA method when it is necessary to reduce a load on the action planning unit in accordance with the traveling environment
(Gao) – “This paper proposes a novel incremental training mode to address the problem of Deep
Reinforcement Learning (DRL) based path planning for a mobile robot. Firstly, we evaluate the
related graphic search algorithms and Reinforcement Learning (RL) algorithms in a lightweight
2D environment. Then, we design the algorithm based on DRL, including observation states,
reward function, network structure as well as parameters optimization, in a 2D environment to
circumvent the time-consuming works for a 3D environment.” (Abstract)
“A mobile robot with autonomous navigation capability can search for one path within specific
time to move from a start point to an endpoint without collision [1]. For indoor environments,
traditional navigation frameworks employ Simultaneous Localization and Mapping (SLAM) [2] to
map unknown environments, then exploit localization algorithms, for instance, Adaptive Monte Carlo Localization (AMCL) [3] to determine the current location of a robot and move it to the destination through a path planning module [4,5]. Path planning is one of the key technologies in navigation system, which usually includes the global as well as local planners [6,7]. The global planner searches the global map to obtain a feasible path by means of planning algorithms, such as Dijkstra, A-star (A*), PRM, Rapidly Exploring Random Tree (RRT) algorithms, and so on [8–12]. The Dijkstra is a classic algorithm for finding the shortest path between two points due to its optimal capability for global planning. The A* achieves better performance by using heuristics to guide its search, which can be seen as an extension of Dijkstra. A motion planning algorithm sampling-based, PRM, solves the problem of determining a path between a starting configuration and a goal configuration while avoiding collisions. The RRT is an algorithm designed to search non-convex, high-dimensional spaces by randomly building a space-filling tree. However, the local planner is used for real-time collision-free based on the global path by means of planning algorithms, such as Artificial Potential Field (APF), Dynamic Window
Approach (DWA) and Timed Elastic Bands (TEB) [13–15]. The APF treats a robot’s configuration as a point in a potential field that combines attraction to the goal, and repulsion from obstacles to track the safe path. One velocity-based local planner, DWA, calculates the optimal collision-free velocity for a robot required to reach its goal, which translates a Cartesian goal (x,y) into a velocity (v,w) command. The TEB merges the states, control inputs and time intervals into a joint trajectory to enable the path planning of time-optimal trajectories. Most of the global path planning algorithms are suitable for path-finding of static scenes and require prior map information. With the increase of complexity and uncertainty of the environment, their efficiency will be greatly reduced. The local path planning obtains the surrounding environment information through sensors, and generates a collision-free path in a related small scope with better adaptability and embarrassed local minimum. In conclusion, the traditional path planning suffers from the issues of map-dependence, low real-time performance, or expert experience-dependence [16].” (Pg 1-2 Sec 1)
“In recent years, there is a growing trend to apply DRL for mobile robot indoor path planning with much success achieved [17]. DRL has the advantages on map-free, strong learning ability and low sensor accuracy dependence, etc., whereas the prohibitively long training duration for DRL-based path planning has severely hindered its wide application to mobile robots, especially for the scenario of limited computation resources.” (Pg 2 Sec 1)
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the information processing device of Takemura with the method for path planning of Gao. One of ordinary skill in the art would have been motivated to make these modifications because “Experimental results show that the incremental training mode can notably improve the development efficiency” (Gao Abstract)
Claim 15:
Takemura in combination with the references relied upon in Claim 13 teach those respective limitations. Takemura does not explicitly teach the following limitations in whole. Gao, in the same field of endeavor of vehicle control, teaches:
wherein the recognition action control unit switches the action planning algorithm of the action planning unit [to the reinforcement learning method when it is necessary to smooth a motion of the mobile apparatus along the travel route or it is necessary to avoid an unknown obstacle in accordance with the traveling environment].
(Takemura) – “First, constituent elements will be described by use of FIG. 1. As one embodiment for executing, by means of time sharing, the plurality of applications (hereinafter, may be described as "multi-applications") capable of dynamically switching the processing cycles of the application based on determination at the normal time or at the time of emergency in vehicle traveling, shown is a configuration of an image processing device (hereinafter, may be described as a "road-environment recognition image processing device") for executing environment recognition of a road by use of information from the on-vehicle camera.” (Para 0076)
“The road-environment recognition image processing device includes a processing section 100 for processing an image from a measurement device 101 such as the camera or the radar, an own-vehicle traveling information section 201, and a control/alarm determining section 300. The processing section 100 processes the information from a device such as the camera or the radar; detects a situation of the road; and make determination. For example, the processing section 100 receives from the measurement device the information for detecting and determining an obstacle including a human or an alternate vehicle in terms of traveling; a white line or a road shoulder, and further, a mark or a signal etc.; or a road state etc. such as a curve.” (Para 0077)
“This embodiment is an example in which the camera imaging section 101 is used as a representative. The processing section 100 is explained by a content in which image processing is executed by using the image imaged by the camera imaging section 101. However, with the same way of thinking, the processing section 100 is also applicable to the processing by use of other information. Based on the determination of the control/alarm determining section 300, a multi-application controlling section 111 determines to execute the processing cycles or operation of the respective functions (described as "ON"); to stop execution (described as "OFF"); or the like. Based on the result, a multi-application executing section 121 executes the respective functions. In the case where a recognition result by the on-vehicle camera is used to execute vehicle control such as collision avoidance or traffic-lane departure avoidance, it is desirable for a recognition processing cycle of the camera to be shorter, compared with the time of normal traveling. Contrarily, at the time of normal traveling having been prepared for the emergency as described above, it is not so much necessary to recognize an external world of the vehicle with the short processing cycle as at the time of emergency. The multi-application controlling section 111 controls the processing cycle or ON/OFF of execution operation, thereby enabling to execute the recognition processing by the camera concerning the collision avoidance or the traffic-lane departure avoidance at the time of emergency with the short processing cycle. Meanwhile, at the time of normal traveling of the vehicle, it is possible to operate the application which pursues the convenience by means of time sharing, and to utilize limited processing capacity of a processor more effectively.” (Para 0078)
“The own-vehicle traveling information section 201 transmits the information regarding the traveling of the own vehicle via an on-vehicle communication network to the processing section 100. The own-vehicle traveling information section 201 includes the vehicle-speed sensor 211, the rudder-angle sensor 212, a turning indicator 213, application operation 214, and the car navigation system 215, of the own vehicle.” (Para 0085)
“The switching of execution conditions of the application as described above is based on the switching of the traveling environments from the general-road traveling to the expressway traveling. The switching of the traveling environments in this way leads to the change of the applications of to be executed, and leads to the change of the priority or other execution conditions. Methods for detecting the switching of the traveling environments include, for example, the switching of a gear change, the change in vehicle speed, passing through a tollgate of a toll road such as the expressway, or the information from the car navigation system.” (Para 0091)
Takemura does not explicitly teach:
to the reinforcement learning method when it is necessary to smooth a motion of the mobile apparatus along the travel route or it is necessary to avoid an unknown obstacle in accordance with the traveling environment
Gao, in the same field of endeavor of vehicle control, teaches:
to the reinforcement learning method when it is necessary to smooth a motion of the mobile apparatus along the travel route or it is necessary to avoid an unknown obstacle in accordance with the traveling environment
(Gao) – “This paper proposes a novel incremental training mode to address the problem of Deep
Reinforcement Learning (DRL) based path planning for a mobile robot. Firstly, we evaluate the
related graphic search algorithms and Reinforcement Learning (RL) algorithms in a lightweight
2D environment. Then, we design the algorithm based on DRL, including observation states,
reward function, network structure as well as parameters optimization, in a 2D environment to
circumvent the time-consuming works for a 3D environment.” (Abstract)
“A mobile robot with autonomous navigation capability can search for one path within specific
time to move from a start point to an endpoint without collision [1]. For indoor environments,
traditional navigation frameworks employ Simultaneous Localization and Mapping (SLAM) [2] to
map unknown environments, then exploit localization algorithms, for instance, Adaptive Monte Carlo Localization (AMCL) [3] to determine the current location of a robot and move it to the destination through a path planning module [4,5]. Path planning is one of the key technologies in navigation system, which usually includes the global as well as local planners [6,7]. The global planner searches the global map to obtain a feasible path by means of planning algorithms, such as Dijkstra, A-star (A*), PRM, Rapidly Exploring Random Tree (RRT) algorithms, and so on [8–12]. The Dijkstra is a classic algorithm for finding the shortest path between two points due to its optimal capability for global planning. The A* achieves better performance by using heuristics to guide its search, which can be seen as an extension of Dijkstra. A motion planning algorithm sampling-based, PRM, solves the problem of determining a path between a starting configuration and a goal configuration while avoiding collisions. The RRT is an algorithm designed to search non-convex, high-dimensional spaces by randomly building a space-filling tree. However, the local planner is used for real-time collision-free based on the global path by means of planning algorithms, such as Artificial Potential Field (APF), Dynamic Window
Approach (DWA) and Timed Elastic Bands (TEB) [13–15]. The APF treats a robot’s configuration as a point in a potential field that combines attraction to the goal, and repulsion from obstacles to track the safe path. One velocity-based local planner, DWA, calculates the optimal collision-free velocity for a robot required to reach its goal, which translates a Cartesian goal (x,y) into a velocity (v,w) command. The TEB merges the states, control inputs and time intervals into a joint trajectory to enable the path planning of time-optimal trajectories. Most of the global path planning algorithms are suitable for path-finding of static scenes and require prior map information. With the increase of complexity and uncertainty of the environment, their efficiency will be greatly reduced. The local path planning obtains the surrounding environment information through sensors, and generates a collision-free path in a related small scope with better adaptability and embarrassed local minimum. In conclusion, the traditional path planning suffers from the issues of map-dependence, low real-time performance, or expert experience-dependence [16].” (Pg 1-2 Sec 1)
“In recent years, there is a growing trend to apply DRL for mobile robot indoor path planning with much success achieved [17]. DRL has the advantages on map-free, strong learning ability and low sensor accuracy dependence, etc., whereas the prohibitively long training duration for DRL-based path planning has severely hindered its wide application to mobile robots, especially for the scenario of limited computation resources.” (Pg 2 Sec 1)
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the information processing device of Takemura with the method for path planning of Gao. One of ordinary skill in the art would have been motivated to make these modifications because “Experimental results show that the incremental training mode can notably improve the development efficiency” (Gao Abstract)
Claim 16:
Takemura in combination with the references relied upon in Claim 13 teach those respective limitations. Takemura does not explicitly teach the following limitations in whole. Gao, in the same field of endeavor of vehicle control, teaches:
wherein the recognition action control unit switches the action planning algorithm of the action planning unit to [the LTP method when it is necessary to plan a travel route that enables avoidance of the obstacle with higher accuracy than predetermined accuracy in accordance with the traveling environment].
(Takemura) – “First, constituent elements will be described by use of FIG. 1. As one embodiment for executing, by means of time sharing, the plurality of applications (hereinafter, may be described as "multi-applications") capable of dynamically switching the processing cycles of the application based on determination at the normal time or at the time of emergency in vehicle traveling, shown is a configuration of an image processing device (hereinafter, may be described as a "road-environment recognition image processing device") for executing environment recognition of a road by use of information from the on-vehicle camera.” (Para 0076)
“The road-environment recognition image processing device includes a processing section 100 for processing an image from a measurement device 101 such as the camera or the radar, an own-vehicle traveling information section 201, and a control/alarm determining section 300. The processing section 100 processes the information from a device such as the camera or the radar; detects a situation of the road; and make determination. For example, the processing section 100 receives from the measurement device the information for detecting and determining an obstacle including a human or an alternate vehicle in terms of traveling; a white line or a road shoulder, and further, a mark or a signal etc.; or a road state etc. such as a curve.” (Para 0077)
“This embodiment is an example in which the camera imaging section 101 is used as a representative. The processing section 100 is explained by a content in which image processing is executed by using the image imaged by the camera imaging section 101. However, with the same way of thinking, the processing section 100 is also applicable to the processing by use of other information. Based on the determination of the control/alarm determining section 300, a multi-application controlling section 111 determines to execute the processing cycles or operation of the respective functions (described as "ON"); to stop execution (described as "OFF"); or the like. Based on the result, a multi-application executing section 121 executes the respective functions. In the case where a recognition result by the on-vehicle camera is used to execute vehicle control such as collision avoidance or traffic-lane departure avoidance, it is desirable for a recognition processing cycle of the camera to be shorter, compared with the time of normal traveling. Contrarily, at the time of normal traveling having been prepared for the emergency as described above, it is not so much necessary to recognize an external world of the vehicle with the short processing cycle as at the time of emergency. The multi-application controlling section 111 controls the processing cycle or ON/OFF of execution operation, thereby enabling to execute the recognition processing by the camera concerning the collision avoidance or the traffic-lane departure avoidance at the time of emergency with the short processing cycle. Meanwhile, at the time of normal traveling of the vehicle, it is possible to operate the application which pursues the convenience by means of time sharing, and to utilize limited processing capacity of a processor more effectively.” (Para 0078)
“The own-vehicle traveling information section 201 transmits the information regarding the traveling of the own vehicle via an on-vehicle communication network to the processing section 100. The own-vehicle traveling information section 201 includes the vehicle-speed sensor 211, the rudder-angle sensor 212, a turning indicator 213, application operation 214, and the car navigation system 215, of the own vehicle.” (Para 0085)
“The switching of execution conditions of the application as described above is based on the switching of the traveling environments from the general-road traveling to the expressway traveling. The switching of the traveling environments in this way leads to the change of the applications of to be executed, and leads to the change of the priority or other execution conditions. Methods for detecting the switching of the traveling environments include, for example, the switching of a gear change, the change in vehicle speed, passing through a tollgate of a toll road such as the expressway, or the information from the car navigation system.” (Para 0091)
Takemura does not explicitly teach:
the LTP method when it is necessary to plan a travel route that enables avoidance of the obstacle with higher accuracy than predetermined accuracy in accordance with the traveling environment
Gao, in the same field of endeavor of vehicle control, teaches:
the LTP method when it is necessary to plan a travel route that enables avoidance of the obstacle with higher accuracy than predetermined accuracy in accordance with the traveling environment
(Gao) – “This paper proposes a novel incremental training mode to address the problem of Deep
Reinforcement Learning (DRL) based path planning for a mobile robot. Firstly, we evaluate the
related graphic search algorithms and Reinforcement Learning (RL) algorithms in a lightweight
2D environment. Then, we design the algorithm based on DRL, including observation states,
reward function, network structure as well as parameters optimization, in a 2D environment to
circumvent the time-consuming works for a 3D environment.” (Abstract)
“A mobile robot with autonomous navigation capability can search for one path within specific
time to move from a start point to an endpoint without collision [1]. For indoor environments,
traditional navigation frameworks employ Simultaneous Localization and Mapping (SLAM) [2] to
map unknown environments, then exploit localization algorithms, for instance, Adaptive Monte Carlo Localization (AMCL) [3] to determine the current location of a robot and move it to the destination through a path planning module [4,5]. Path planning is one of the key technologies in navigation system, which usually includes the global as well as local planners [6,7]. The global planner searches the global map to obtain a feasible path by means of planning algorithms, such as Dijkstra, A-star (A*), PRM, Rapidly Exploring Random Tree (RRT) algorithms, and so on [8–12]. The Dijkstra is a classic algorithm for finding the shortest path between two points due to its optimal capability for global planning. The A* achieves better performance by using heuristics to guide its search, which can be seen as an extension of Dijkstra. A motion planning algorithm sampling-based, PRM, solves the problem of determining a path between a starting configuration and a goal configuration while avoiding collisions. The RRT is an algorithm designed to search non-convex, high-dimensional spaces by randomly building a space-filling tree. However, the local planner is used for real-time collision-free based on the global path by means of planning algorithms, such as Artificial Potential Field (APF), Dynamic Window
Approach (DWA) and Timed Elastic Bands (TEB) [13–15]. The APF treats a robot’s configuration as a point in a potential field that combines attraction to the goal, and repulsion from obstacles to track the safe path. One velocity-based local planner, DWA, calculates the optimal collision-free velocity for a robot required to reach its goal, which translates a Cartesian goal (x,y) into a velocity (v,w) command. The TEB merges the states, control inputs and time intervals into a joint trajectory to enable the path planning of time-optimal trajectories. Most of the global path planning algorithms are suitable for path-finding of static scenes and require prior map information. With the increase of complexity and uncertainty of the environment, their efficiency will be greatly reduced. The local path planning obtains the surrounding environment information through sensors, and generates a collision-free path in a related small scope with better adaptability and embarrassed local minimum. In conclusion, the traditional path planning suffers from the issues of map-dependence, low real-time performance, or expert experience-dependence [16].” (Pg 1-2 Sec 1)
“In recent years, there is a growing trend to apply DRL for mobile robot indoor path planning with much success achieved [17]. DRL has the advantages on map-free, strong learning ability and low sensor accuracy dependence, etc., whereas the prohibitively long training duration for DRL-based path planning has severely hindered its wide application to mobile robots, especially for the scenario of limited computation resources.” (Pg 2 Sec 1)
Therefore, it would be obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the information processing device of Takemura with the method for path planning of Gao. One of ordinary skill in the art would have been motivated to make these modifications because “Experimental results show that the incremental training mode can notably improve the development efficiency” (Gao Abstract)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Kim (US20210009161) teaches a similar path providing device for a vehicle.
Oba (US2000139992) teaches a related vehicle control method.
Oyama (US11609568) teaches a similar travel control system.
Abouali (US20220340133) teaches switching operation modes based on visibility.
Kaneo (US20200086889) teaches a related vehicle control method.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DAVID RUBEN PEDERSEN whose telephone number is (571)272-9696. The examiner can normally be reached M-Th: 07:00 -16:00 Eastern.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ramon Mercado can be reached at (571) 270-5744. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DAVID RUBEN PEDERSEN/Examiner, Art Unit 3658
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