DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is reply to the Application Number 18/937,634 filed on 11/05/2024
Claims 1 – 13 are currently pending and have been examined
This action is made NON-FINAL
Priority
Acknowledgement is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference character “100” in Fig. 3 has been used to designate both a vehicle and trailer. Furthermore, reference character “200” in Fig. 3 has been used to designate both a trailer and an arrow.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Objections
Claims 1 – 13 are objected to because of the following informalities:
Claim 1, line 5 states: “activating, by the processor, an autonomous driving based”. Claim 7, line 6 states: “activating an autonomous driving based on the acquired”. Claim 8, line 5 states: “activate an autonomous driving based on the acquired signal”. For all of these claims, it is improper to state “an autonomous driving” as “autonomous driving” is not a noun but a verb.
Claims 2 – 6 are also objected as being dependent upon claim 1. Claims 9 – 13 are also objected as being dependent upon claim 8.
Appropriate correction is required.
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.
Claims 1 – 13 are 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 1 states: “learning, by the processor, an acceleration pattern of the autonomous vehicle based on the activated autonomous driving;”, however it is indefinite on how the processor is able to learn the acceleration pattern of the autonomous vehicle. For example, “learning” can be interpreted as storing acceleration data when it is in an autonomous driving mode or an AI model being used to evaluate the acceleration pattern. Furthermore, a processor is known to one with knowledge in the art to process received information however it is indefinite on how it is able to learn the acceleration pattern within the context of the claims. The specification state in paragraph 0053 that a deep learning model is used for learning however that cannot be interpreted within the context of the claims. Claim 7 states: “A non-transitory computer-readable recording medium having a program recorded thereon, the program to direct a processor to perform acts of: … learning an acceleration pattern of the autonomous vehicle based on the activated autonomous driving;”, which states the same indefiniteness of how a non-transitory computer-readable medium having a program recorded is able to learn the acceleration pattern. Furthermore, claim 8 states: “An autonomous vehicle comprising a processor, wherein the processor is configured to: … learn a speed-specific acceleration pattern of the autonomous vehicle based on the activated autonomous driving;” which again states the same indefiniteness found in claim 1 and therefore rejected under the same pretenses.
Claims 2 – 6 are also rejected as being dependent upon claim 1. Claims 9 – 13 are also rejected as being dependent upon claim 8.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
Claims 1 – 5 and 7 – 12 are rejected under 35 U.S.C. 103 as being unpatentable over Kang et al. (US 11667307 B2) further in view of Smalley et al. (US 20250074440 A1).
Regarding claim 1, Kang teaches a method of controlling an autonomous vehicle, the method comprising: (Kang: Abstract: “A method for controlling autonomous driving for an autonomous driving vehicle,”)
acquiring, by a processor, (Kang: Col. 11, lines 58 – 62: “The operations of the methods or algorithms described in connection with the processor embodiments disclosed in the present disclosure may be directly implemented with a hardware module, a software module, or the combinations thereof, executed by the processor.”)
a signal indicative of connection between the autonomous vehicle and a trailer; (Kang: Col. 2, lines 24 – 30: “According to an embodiment, the determining of the trailer correcting parameter based on whether the trailer is attached to the autonomous driving vehicle may include determining whether the trailer is attached, by analyzing the sensing information on the autonomous driving, calculating a specification of the trailer based on the sensing information on the autonomous driving, when the trailer is attached”: Col. 7, lines 24 – 32: “The cognition part 222 may recognize a lane based on sensing information from the Radar or Lidar 212 and image information captured by the external camera 213, and may identify a vehicle travelling around a host vehicle, or an obstacle or pedestrian around the host vehicle. In addition, the cognition part 222 may determine whether the trailer is mounted, based on the sensing information from the Radar or Lidar 212 and image information photographed by the external camera 213.”)
activating, by the processor, an autonomous driving based on the acquired signal;
learning, by the processor, an acceleration pattern of the autonomous vehicle based on the activated autonomous driving; (Kang: Col. 10, lines 34 – 49 : “When the trailer is attached as the determination result, the first parameter correcting part 332 may calculate the specification of the trailer based on the sensing information on the autonomous driving (S630). In this case, the specification of the trailer may include information on the length, the volume, and the weight of the trailer, but is not limited thereto. For example, the specification of the trailer may include the shape and the type of the trailer. The first parameter correcting part 332 may determine a trailer correcting parameter P.sub.trail which is calculated by making reference to a preset mapping table of the trailer and corresponds to the specification of the trailer (S640). The trailer mapping table may have the trailer correcting parameter P.sub.trail corresponding to the specification of the trailer and previously defined.”: Col. 11, lines 31 – 57: “According to the present disclosure, the autonomous driving control apparatus 300 may perform a control operation to track the behavior corresponding to a required input value “a_req” required to the real vehicle by applying, to the initial input value “a_raw”, three parameters, which is the controller correcting parameter P.sub.hw, the trailer correcting parameter P.sub.trail, and the driving context correcting parameter P.sub.env, calculated by the control parameter application part 330 and correcting the initial input value “a_raw” as in following equation 1.
a_req=a_raw(P.sub.hw+P.sub.trail+P.sub.env) Equation 1
In addition, according to the present disclosure, the autonomous driving accident may be previously prevented through the strategy to optimize the longitudinal control tracking performance. In addition, the present disclosure may provide a method and an apparatus for controlling autonomous driving, capable of improving the autonomous driving performance in level 3. In addition, according to the present disclosure, the longitudinal control correcting parameter is adaptively applied through the parameter mapping table based on various vehicle specifications and various driving contexts, thereby improving the longitudinal control tracking performance and minimizing maintenance cost.”,
Supplemental Note: the autonomous driving can identify trailer properties to be used to optimize the vehicle’s performance in regards to acceleration)
determining, by the processor, overloading by comparing the learned acceleration pattern with a preset normal pattern; and (Kang: Col. 1, line 61 – Col. 2, line 23: “According to an aspect of the present disclosure, a method for controlling autonomous driving for an autonomous driving vehicle may include collecting sensing information on autonomous driving in an autonomous driving mode, calculating an initial longitudinal control value based on the sensing information on the autonomous driving, correcting the initial longitudinal control value based on the sensing information of the autonomous driving, and performing a longitudinal driving control by transmitting the corrected longitudinal control value to a lower controller. According to an embodiment, the correcting of the initial longitudinal control value based on the sensing information of the autonomous driving may include activating a controller correcting parameter based on an input or output error of the lower controller. According to an embodiment, the activating of the controller correcting parameter based on the input or output error of the lower controller may include measuring the input or output error between a required acceleration and an output acceleration for the lower controller, comparing between the input or output error and a critical error, and activating the controller correcting parameter to maintain tracking performance to be within a range of the critical error, when the input or output error exceeds the critical error, as the comparison result. According to an embodiment, the correcting of the initial longitudinal control value based on the sensing information of the autonomous driving may include determining a trailer correcting parameter based on whether a trailer is attached to the autonomous driving vehicle.”,
Supplemental Note: the system is able to determine the required acceleration based on a trailer attached to the vehicle and therefore able to determine a correcting parameter. The preset normal pattern is interpreted as the required acceleration and output acceleration being the same, which is evaluated for any parameters for the required acceleration needed to be configured. Overloading is interpreted as applying a correction parameter to the acceleration of the vehicle)
determining, by the processor, whether to maintain the activated autonomous driving (Kang: Col. 7, lines 24 – 32: “The cognition part 222 may recognize a lane based on sensing information from the Radar or Lidar 212 and image information captured by the external camera 213, and may identify a vehicle travelling around a host vehicle, or an obstacle or pedestrian around the host vehicle. In addition, the cognition part 222 may determine whether the trailer is mounted, based on the sensing information from the Radar or Lidar 212 and image information photographed by the external camera 213.”; Col. 7, lines 63 – 67: “The controller 223 may determine whether control needs to be transferred from the system to the driver, based on internal and external states of the vehicle depending on the cognition result of the cognition part 222, and whether the driver inputs a button to release autonomous driving.”)
In sum, Kang teaches a method of controlling an autonomous vehicle, the method comprising: acquiring, by a processor, a signal indicative of connection between the autonomous vehicle and a trailer; activating, by the processor, an autonomous driving based on the acquired signal; learning, by the processor, an acceleration pattern of the autonomous vehicle based on the activated autonomous driving; determining, by the processor, overloading by comparing the learned acceleration pattern with a preset normal pattern; and determining, by the processor, whether to maintain the activated autonomous driving. Kang however does not teach based on a result of the determination on the overloading whereas Smalley does.
Smalley teaches based on a result of the determination on the overloading. (Smalley: Paragraph 0085: “For example, in one or more particular arrangements, the at least one stability evaluation parameter may include a lateral acceleration of the vehicle. A “measured lateral acceleration” a.sub.M of the vehicle 100 may be a lateral acceleration obtained from a sensor (such as lateral acceleration sensor 151) configured to measure or calculate a lateral acceleration of the vehicle, or a lateral acceleration computed using data from a sensor providing information necessary for such a computation.”; Paragraph 0099: “In particular arrangements using yaw rate and lateral acceleration as stability evaluation parameters, “instability” in the vehicle-trailer system may be determined to occur when the total amount of time that the measured lateral acceleration a.sub.M satisfied a predetermined lateral acceleration condition and/or the total amount of time that the measured yaw rate ω.sub.M satisfied a predetermined yaw rate condition exceeds the significant proportion tLIM of the predetermined time period tM. The stability evaluation module 213 may be configured to control operation of the vehicle 100 to generate a stability alert responsive to detection of an instability condition.”; Paragraph 0161: “Unbalanced or overloaded trailers may then produce instability conditions that are detected by the stability evaluation module, prompting generation of alerts and other responses (such as deactivation of a lane tracing assist (LTA) system, for example).”,
Supplemental Note: the overloaded condition causes instability identified by instabilities in lateral acceleration)
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Kang with the teachings of Smalley with a reasonable expectation of success. One of ordinary skill in the art would find it obvious to try to implement the ability to detect instability for a vehicle with an attached trailer based on lateral acceleration parameters as taught by Smalley with the vehicle of Kang. Additionally, Smalley teaches the ability to cancel the autonomous driving function if the instability condition is persistent so the driver is able to manually control a vehicle (Smalley: Paragraph 0066). Detecting instability and returning manual control to the driver mitigates conditions where the instability can cause damage the vehicle or the user. Kang already teaches the ability to gather trailer specification data and adjusting the required acceleration needed to power the vehicle with an attached trailer, thus combining the instability mitigation functions of Smalley increases the functionality of the vehicle to detect a instability while also increasing the safety of the road users.
Regarding claim 2, Kang, as modified, teaches providing information related to the deactivated autonomous driving through a display. (Kang: Col. 8, lines 1 – 5: “The controller 223 may perform a control operation to output a specific warning notification for requesting for transferring the control to the driver, when the control needs to be transferred to the driver.”)
In sum Kang teaches providing information related to the deactivated autonomous driving through a display. Kang however does not teach in response to determination of the overloading, deactivating, by the processor, the autonomous driving whereas Smalley does.
Smalley teaches further comprising: in response to determination of the overloading, (Smalley: Paragraph 0085: “For example, in one or more particular arrangements, the at least one stability evaluation parameter may include a lateral acceleration of the vehicle. A “measured lateral acceleration” a.sub.M of the vehicle 100 may be a lateral acceleration obtained from a sensor (such as lateral acceleration sensor 151) configured to measure or calculate a lateral acceleration of the vehicle, or a lateral acceleration computed using data from a sensor providing information necessary for such a computation.”; Paragraph 0099: “In particular arrangements using yaw rate and lateral acceleration as stability evaluation parameters, “instability” in the vehicle-trailer system may be determined to occur when the total amount of time that the measured lateral acceleration a.sub.M satisfied a predetermined lateral acceleration condition and/or the total amount of time that the measured yaw rate ω.sub.M satisfied a predetermined yaw rate condition exceeds the significant proportion tLIM of the predetermined time period tM. The stability evaluation module 213 may be configured to control operation of the vehicle 100 to generate a stability alert responsive to detection of an instability condition.”; Paragraph 0161: “Unbalanced or overloaded trailers may then produce instability conditions that are detected by the stability evaluation module, prompting generation of alerts and other responses (such as deactivation of a lane tracing assist (LTA) system, for example).”,
Supplemental Note: the overloaded condition causes instability identified by instabilities in lateral acceleration)
deactivating, by the processor, the autonomous driving, and (Smalley: Paragraph 0066: “The stability evaluation module 213 may be configured as described herein to evaluate the results of these computations to determine if an instability condition exists in the vehicle-trailer system. The stability evaluation module 213 may also be configured as described herein to, responsive to a determination that an instability condition exists, autonomously control certain operations of the vehicle 100 to generate instability alerts to users of the vehicle and/or to deactivate certain vehicle systems to facilitate full manual control of the vehicle. These steps may help the user stabilize the motion of the vehicle-trailer system as soon as possible.”)
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Kang with the teachings of Smalley with a reasonable expectation of success. As stated for claim 1, one of ordinary skill in the art would find it obvious to try to implement the ability to detect instability for a vehicle and trailer based on lateral acceleration parameters as taught by Smalley with the vehicle of Kang. Additionally, Smalley teaches the ability to cancel the autonomous driving function if the instability condition is persistent so the driver is able to manually control a vehicle (Smalley: Paragraph 0066). Detecting instability and returning manual control to the driver mitigates conditions where the instability can cause damage the vehicle or the user. Kang already teaches the ability to gather trailer specification data and adjusting the required acceleration needed to power the vehicle with an attached trailer, thus combining the instability mitigation functions of Smalley increases the functionality of the vehicle to detect a instability while also increasing the safety of the road users.
Regarding claim 3, Kang, as modified, teaches further comprising:
in response to determination that there is no overloading, maintaining, by the processor, the activated autonomous driving. (Kang: Col. 10, lines 34 – 49 : “When the trailer is attached as the determination result, the first parameter correcting part 332 may calculate the specification of the trailer based on the sensing information on the autonomous driving (S630). In this case, the specification of the trailer may include information on the length, the volume, and the weight of the trailer, but is not limited thereto. For example, the specification of the trailer may include the shape and the type of the trailer. The first parameter correcting part 332 may determine a trailer correcting parameter P.sub.trail which is calculated by making reference to a preset mapping table of the trailer and corresponds to the specification of the trailer (S640). The trailer mapping table may have the trailer correcting parameter P.sub.trail corresponding to the specification of the trailer and previously defined.”; Col. 11, lines 31 – 57: “According to the present disclosure, the autonomous driving control apparatus 300 may perform a control operation to track the behavior corresponding to a required input value “a_req” required to the real vehicle by applying, to the initial input value “a_raw”, three parameters, which is the controller correcting parameter P.sub.hw, the trailer correcting parameter P.sub.trail, and the driving context correcting parameter P.sub.env, calculated by the control parameter application part 330 and correcting the initial input value “a_raw” as in following equation 1.
a_req=a_raw(P.sub.hw+P.sub.trail+P.sub.env) Equation 1
In addition, according to the present disclosure, the autonomous driving accident may be previously prevented through the strategy to optimize the longitudinal control tracking performance. In addition, the present disclosure may provide a method and an apparatus for controlling autonomous driving, capable of improving the autonomous driving performance in level 3. In addition, according to the present disclosure, the longitudinal control correcting parameter is adaptively applied through the parameter mapping table based on various vehicle specifications and various driving contexts, thereby improving the longitudinal control tracking performance and minimizing maintenance cost.”,
Supplemental Note: the autonomous driving can identify trailer properties to be used to optimize the vehicle’s performance. The autonomous driving in the cited system is able to perform in conditions interpreted as overloading and conditions operating normally. For example, a_req=a_raw if there are no other correction parameters detected)
Regarding claim 4, Kang, as modified, teaches wherein the preset normal pattern comprises a first normal pattern and a second normal pattern, and (Kang: Col. 3, lines 62 – 67: “According to an embodiment, the corrected control value may be calculated by applying at least one of the controller correcting parameter, the trailer correcting parameter, or the driving context correcting parameter to the initial longitudinal control value.”: Col. 7, lines 29 – 32: “In addition, the cognition part 222 may determine whether the trailer is mounted, based on the sensing information from the Radar or Lidar 212 and image information photographed by the external camera 213.”: Col. 8, lines 47 – 63: “The sensing part 310 may include various sensors for autonomous driving and may generate sensing information on the autonomous driving, based on sensing information collected from the sensors. The sensing information on the autonomous driving may include positioning information, precision map information, camera capturing information, the sensing information by the Radar or Lidar, weather information, driving speed information, information on the recognized driver gaze, sensing information on vehicle internal failure, information on a button input to release the autonomous driving, sensing information on the operation of the steering wheel, or the sensing information on the operation of the acceleration or deceleration pedal, but is not limited thereto. The longitudinal control value generator 320 may generate an initial longitudinal control value “a_raw” based on the sensing information on the autonomous driving.”,
Supplemental Note: the preset normal patterns are interpreted as the initial acceleration patterns detected by the sensing part. This can include a vehicle with or without a trailer which both have an “a_raw” value, thus two normal patterns which are set per the initial conditions acquired by the sensing part)
… are determined as being connected based on the signal. (Kang: Col. 2, lines 24 – 30: “According to an embodiment, the determining of the trailer correcting parameter based on whether the trailer is attached to the autonomous driving vehicle may include determining whether the trailer is attached, by analyzing the sensing information on the autonomous driving, calculating a specification of the trailer based on the sensing information on the autonomous driving, when the trailer is attached”: Col. 7, lines 24 – 32: “The cognition part 222 may recognize a lane based on sensing information from the Radar or Lidar 212 and image information captured by the external camera 213, and may identify a vehicle travelling around a host vehicle, or an obstacle or pedestrian around the host vehicle. In addition, the cognition part 222 may determine whether the trailer is mounted, based on the sensing information from the Radar or Lidar 212 and image information photographed by the external camera 213.”)
In sum, Kang teaches wherein the preset normal pattern comprises a first normal pattern and a second normal pattern, and the autonomous vehicle and the trailer are determined as being connected based on the signal. Kang however does not fully teach wherein determining overloading comprises determining the overloading based on the first normal pattern when the autonomous vehicle and the trailer are connected whereas Smalley does.
Smalley teaches wherein determining overloading comprises determining the overloading based on the first normal pattern when the autonomous vehicle and the trailer (Smalley: Paragraph 0017: “Embodiments described herein relate to a system for detecting an instability condition in a moving vehicle-trailer system. The stability monitoring-system includes a processor and a memory communicably coupled to the processor and storing a stability evaluation module including computer-readable instructions that when executed by the processor cause the processor to determine if a measured lateral acceleration of the vehicle satisfies a predetermined lateral acceleration condition and/or a measured yaw rate of the vehicle satisfies a predetermined yaw rate condition. The presence of either (or both of) a predetermined lateral acceleration condition and/or a predetermined yaw rate condition may indicate an instability condition in the vehicle-trailer system. If the measured lateral acceleration satisfies a predetermined lateral acceleration condition, the stability evaluation module may determine a total amount of time within a predetermined time period that the measured lateral acceleration satisfies the predetermined lateral acceleration condition. If the measured yaw rate satisfies a predetermined yaw rate condition, the stability evaluation module may determine a total amount of time within a predetermined time period that the measured yaw rate satisfies the predetermined yaw rate condition.”; Paragraph 0161: “Testing using embodiments of the stability evaluation module described herein has indicated that the stability/instability determinations provided by the module are relatively insensitive to variations in the unloaded trailer parameters, and depend primarily on towing vehicle parameters and parameters of the trailer in a loaded condition. Unbalanced or overloaded trailers may then produce instability conditions that are detected by the stability evaluation module, prompting generation of alerts and other responses (such as deactivation of a lane tracing assist (LTA) system, for example).”,
Supplemental Note: based on the different parameters, including lateral acceleration, the system is able to determine if a predetermined condition is present to cause instability. The conditions without any instability issues and normal lateral acceleration with the trailer are interpreted as the first normal pattern. Overloaded vehicles are taught to produce instability)
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Kang with the teachings of Smalley with a reasonable expectation of success. One of ordinary skill in the art would find combining Smalley’s teaching of identifying when a vehicle is overloaded or not and determine if there any predetermined conditions to prevent instability with the vehicle system of Kang as a simple substitution. Smalley and Kang both teach the ability to detect any abnormal parameters and apply a corrective parameter to control the vehicle, Smalley determines a time duration in which the stability is met (Smalley: Paragraph 0004) while Kang teaches adjusting it’s required acceleration as to not be in a critical error zone (Kang: Col. 2, lines 9 – 18: Col. 3, lines 22 – 29). Prior to Smalley determining an instability, the vehicle is operating under normal conditions with the attached trailer which is interpreted as the vehicle of Kang performing under normal conditions when a critical error zone is not identified. This is a simple substitution as prior to any instability measures being performed by Smalley or Kang, the vehicle is performing under normal conditions.
Regarding claim 5, Kang, as modified, teaches wherein determining overloading further comprises determining the overloading based on the second normal pattern when the autonomous vehicle and the trailer are determined as not being connected based on the signal. (Kang: Col. 10, lines 34 – 49 : “When the trailer is attached as the determination result, the first parameter correcting part 332 may calculate the specification of the trailer based on the sensing information on the autonomous driving (S630). In this case, the specification of the trailer may include information on the length, the volume, and the weight of the trailer, but is not limited thereto. For example, the specification of the trailer may include the shape and the type of the trailer. The first parameter correcting part 332 may determine a trailer correcting parameter P.sub.trail which is calculated by making reference to a preset mapping table of the trailer and corresponds to the specification of the trailer (S640). The trailer mapping table may have the trailer correcting parameter P.sub.trail corresponding to the specification of the trailer and previously defined.”; Col. 11, lines 31 – 57: “According to the present disclosure, the autonomous driving control apparatus 300 may perform a control operation to track the behavior corresponding to a required input value “a_req” required to the real vehicle by applying, to the initial input value “a_raw”, three parameters, which is the controller correcting parameter P.sub.hw, the trailer correcting parameter P.sub.trail, and the driving context correcting parameter P.sub.env, calculated by the control parameter application part 330 and correcting the initial input value “a_raw” as in following equation 1.
a_req=a_raw(P.sub.hw+P.sub.trail+P.sub.env) Equation 1
In addition, according to the present disclosure, the autonomous driving accident may be previously prevented through the strategy to optimize the longitudinal control tracking performance. In addition, the present disclosure may provide a method and an apparatus for controlling autonomous driving, capable of improving the autonomous driving performance in level 3. In addition, according to the present disclosure, the longitudinal control correcting parameter is adaptively applied through the parameter mapping table based on various vehicle specifications and various driving contexts, thereby improving the longitudinal control tracking performance and minimizing maintenance cost.”,
Supplemental Note: the autonomous driving can identify trailer properties to be used to optimize the vehicle’s performance. The autonomous driving in the cited system is able to perform in conditions interpreted as overloading and conditions operating normally. For example, a_req=a_raw if there are no other correction parameters detected such as the trailer not being attached and no need for a P.sub.trail correction parameter)
Regarding claim 7, Kang teaches a non-transitory computer-readable recording medium having a program recorded thereon, the program to direct a processor to perform acts of: (Kang: Col. 11, lines 58 – 62: “The operations of the methods or algorithms described in connection with the processor embodiments disclosed in the present disclosure may be directly implemented with a hardware module, a software module, or the combinations thereof, executed by the processor.”; Col. 12, lines 1 – 9 :“The exemplary storage medium may be coupled to the processor. The processor may read out information from the storage medium and may write information in the storage medium. Alternatively, the storage medium may be integrated with the processor. The processor and storage medium may reside in an application specific integrated circuit (ASIC). The ASIC may reside in a user terminal. Alternatively, the processor and the storage medium may reside as separate components of the terminal of the user.”)
acquiring a signal indicative of connection between an autonomous vehicle and a trailer; (Kang: Col. 2, lines 24 – 30: “According to an embodiment, the determining of the trailer correcting parameter based on whether the trailer is attached to the autonomous driving vehicle may include determining whether the trailer is attached, by analyzing the sensing information on the autonomous driving, calculating a specification of the trailer based on the sensing information on the autonomous driving, when the trailer is attached”: Col. 7, lines 24 – 32: “The cognition part 222 may recognize a lane based on sensing information from the Radar or Lidar 212 and image information captured by the external camera 213, and may identify a vehicle travelling around a host vehicle, or an obstacle or pedestrian around the host vehicle. In addition, the cognition part 222 may determine whether the trailer is mounted, based on the sensing information from the Radar or Lidar 212 and image information photographed by the external camera 213.”)
activating an autonomous driving based on the acquired signal;
learning an acceleration pattern of the autonomous vehicle based on the activated autonomous driving; (Kang: Col. 10, lines 34 – 49 : “When the trailer is attached as the determination result, the first parameter correcting part 332 may calculate the specification of the trailer based on the sensing information on the autonomous driving (S630). In this case, the specification of the trailer may include information on the length, the volume, and the weight of the trailer, but is not limited thereto. For example, the specification of the trailer may include the shape and the type of the trailer. The first parameter correcting part 332 may determine a trailer correcting parameter P.sub.trail which is calculated by making reference to a preset mapping table of the trailer and corresponds to the specification of the trailer (S640). The trailer mapping table may have the trailer correcting parameter P.sub.trail corresponding to the specification of the trailer and previously defined.”: Col. 11, lines 31 – 57: “According to the present disclosure, the autonomous driving control apparatus 300 may perform a control operation to track the behavior corresponding to a required input value “a_req” required to the real vehicle by applying, to the initial input value “a_raw”, three parameters, which is the controller correcting parameter P.sub.hw, the trailer correcting parameter P.sub.trail, and the driving context correcting parameter P.sub.env, calculated by the control parameter application part 330 and correcting the initial input value “a_raw” as in following equation 1.
a_req=a_raw(P.sub.hw+P.sub.trail+P.sub.env) Equation 1
In addition, according to the present disclosure, the autonomous driving accident may be previously prevented through the strategy to optimize the longitudinal control tracking performance. In addition, the present disclosure may provide a method and an apparatus for controlling autonomous driving, capable of improving the autonomous driving performance in level 3. In addition, according to the present disclosure, the longitudinal control correcting parameter is adaptively applied through the parameter mapping table based on various vehicle specifications and various driving contexts, thereby improving the longitudinal control tracking performance and minimizing maintenance cost.”,
Supplemental Note: the autonomous driving can identify trailer properties to be used to optimize the vehicle’s performance in regards to acceleration)
determining overloading by comparing the learned acceleration pattern with a preset normal pattern; and (Kang: Col. 1, line 61 – Col. 2, line 23: “According to an aspect of the present disclosure, a method for controlling autonomous driving for an autonomous driving vehicle may include collecting sensing information on autonomous driving in an autonomous driving mode, calculating an initial longitudinal control value based on the sensing information on the autonomous driving, correcting the initial longitudinal control value based on the sensing information of the autonomous driving, and performing a longitudinal driving control by transmitting the corrected longitudinal control value to a lower controller. According to an embodiment, the correcting of the initial longitudinal control value based on the sensing information of the autonomous driving may include activating a controller correcting parameter based on an input or output error of the lower controller. According to an embodiment, the activating of the controller correcting parameter based on the input or output error of the lower controller may include measuring the input or output error between a required acceleration and an output acceleration for the lower controller, comparing between the input or output error and a critical error, and activating the controller correcting parameter to maintain tracking performance to be within a range of the critical error, when the input or output error exceeds the critical error, as the comparison result. According to an embodiment, the correcting of the initial longitudinal control value based on the sensing information of the autonomous driving may include determining a trailer correcting parameter based on whether a trailer is attached to the autonomous driving vehicle.”,
Supplemental Note: the system is able to determine the required acceleration based on a trailer attached to the vehicle and therefore able to determine a correcting parameter. The preset normal pattern is interpreted as the required acceleration and output acceleration being the same, which is evaluated for any parameters needed to be corrected. Overloading is interpreted as applying a correction parameter to the acceleration of the vehicle based on any critical conditions)
determining whether to maintain the activated autonomous driving (Kang: Col. 7, lines 24 – 32: “The cognition part 222 may recognize a lane based on sensing information from the Radar or Lidar 212 and image information captured by the external camera 213, and may identify a vehicle travelling around a host vehicle, or an obstacle or pedestrian around the host vehicle. In addition, the cognition part 222 may determine whether the trailer is mounted, based on the sensing information from the Radar or Lidar 212 and image information photographed by the external camera 213.”; Col. 7, lines 63 – 67: “The controller 223 may determine whether control needs to be transferred from the system to the driver, based on internal and external states of the vehicle depending on the cognition result of the cognition part 222, and whether the driver inputs a button to release autonomous driving.”)
In sum, Kang teaches a non-transitory computer-readable recording medium having a program recorded thereon, the program to direct a processor to perform acts of: acquiring a signal indicative of connection between an autonomous vehicle and a trailer; activating an autonomous driving based on the acquired signal; learning an acceleration pattern of the autonomous vehicle based on the activated autonomous driving; determining overloading by comparing the learned acceleration pattern with a preset normal pattern; and determining whether to maintain the activated autonomous driving. Kang however does not teach based on a result of the determination on the overloading whereas Smalley does.
Smalley teaches based on a result of the determination on the overloading. (Smalley: Paragraph 0085: “For example, in one or more particular arrangements, the at least one stability evaluation parameter may include a lateral acceleration of the vehicle. A “measured lateral acceleration” a.sub.M of the vehicle 100 may be a lateral acceleration obtained from a sensor (such as lateral acceleration sensor 151) configured to measure or calculate a lateral acceleration of the vehicle, or a lateral acceleration computed using data from a sensor providing information necessary for such a computation.”; Paragraph 0099: “In particular arrangements using yaw rate and lateral acceleration as stability evaluation parameters, “instability” in the vehicle-trailer system may be determined to occur when the total amount of time that the measured lateral acceleration a.sub.M satisfied a predetermined lateral acceleration condition and/or the total amount of time that the measured yaw rate ω.sub.M satisfied a predetermined yaw rate condition exceeds the significant proportion tLIM of the predetermined time period tM. The stability evaluation module 213 may be configured to control operation of the vehicle 100 to generate a stability alert responsive to detection of an instability condition.”; Paragraph 0161: “Unbalanced or overloaded trailers may then produce instability conditions that are detected by the stability evaluation module, prompting generation of alerts and other responses (such as deactivation of a lane tracing assist (LTA) system, for example).”,
Supplemental Note: the overloaded condition causes instability identified by instabilities in lateral acceleration)
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Kang with the teachings of Smalley with a reasonable expectation of success. Please refer to the rejection of claim 1 as both claim the same function and therefore rejected under the same pretenses.
Regarding claim 8, Kang teaches an autonomous vehicle comprising a processor, wherein the processor is configured to: (Kang: Abstract: “A method for controlling autonomous driving for an autonomous driving vehicle,”; Col. 11, lines 58 – 62: “The operations of the methods or algorithms described in connection with the processor embodiments disclosed in the present disclosure may be directly implemented with a hardware module, a software module, or the combinations thereof, executed by the processor.”)
acquire a signal indicative of connection between the autonomous vehicle and a trailer; (Kang: Col. 2, lines 24 – 30: “According to an embodiment, the determining of the trailer correcting parameter based on whether the trailer is attached to the autonomous driving vehicle may include determining whether the trailer is attached, by analyzing the sensing information on the autonomous driving, calculating a specification of the trailer based on the sensing information on the autonomous driving, when the trailer is attached”: Col. 7, lines 24 – 32: “The cognition part 222 may recognize a lane based on sensing information from the Radar or Lidar 212 and image information captured by the external camera 213, and may identify a vehicle travelling around a host vehicle, or an obstacle or pedestrian around the host vehicle. In addition, the cognition part 222 may determine whether the trailer is mounted, based on the sensing information from the Radar or Lidar 212 and image information photographed by the external camera 213.”)
activate an autonomous driving based on the acquired signal;
learn a speed-specific acceleration pattern of the autonomous vehicle based on the activated autonomous driving; (Kang: Col. 10, lines 34 – 49 : “When the trailer is attached as the determination result, the first parameter correcting part 332 may calculate the specification of the trailer based on the sensing information on the autonomous driving (S630). In this case, the specification of the trailer may include information on the length, the volume, and the weight of the trailer, but is not limited thereto. For example, the specification of the trailer may include the shape and the type of the trailer. The first parameter correcting part 332 may determine a trailer correcting parameter P.sub.trail which is calculated by making reference to a preset mapping table of the trailer and corresponds to the specification of the trailer (S640). The trailer mapping table may have the trailer correcting parameter P.sub.trail corresponding to the specification of the trailer and previously defined.”: Col. 11, lines 31 – 57: “According to the present disclosure, the autonomous driving control apparatus 300 may perform a control operation to track the behavior corresponding to a required input value “a_req” required to the real vehicle by applying, to the initial input value “a_raw”, three parameters, which is the controller correcting parameter P.sub.hw, the trailer correcting parameter P.sub.trail, and the driving context correcting parameter P.sub.env, calculated by the control parameter application part 330 and correcting the initial input value “a_raw” as in following equation 1.
a_req=a_raw(P.sub.hw+P.sub.trail+P.sub.env) Equation 1
In addition, according to the present disclosure, the autonomous driving accident may be previously prevented through the strategy to optimize the longitudinal control tracking performance. In addition, the present disclosure may provide a method and an apparatus for controlling autonomous driving, capable of improving the autonomous driving performance in level 3. In addition, according to the present disclosure, the longitudinal control correcting parameter is adaptively applied through the parameter mapping table based on various vehicle specifications and various driving contexts, thereby improving the longitudinal control tracking performance and minimizing maintenance cost.”,
Supplemental Note: the autonomous driving can identify trailer properties to be used to optimize the vehicle’s performance in regards to acceleration)
determine overloading by comparing and analyzing the learned acceleration pattern and a preset normal pattern; and (Kang: Col. 1, line 61 – Col. 2, line 23: “According to an aspect of the present disclosure, a method for controlling autonomous driving for an autonomous driving vehicle may include collecting sensing information on autonomous driving in an autonomous driving mode, calculating an initial longitudinal control value based on the sensing information on the autonomous driving, correcting the initial longitudinal control value based on the sensing information of the autonomous driving, and performing a longitudinal driving control by transmitting the corrected longitudinal control value to a lower controller. According to an embodiment, the correcting of the initial longitudinal control value based on the sensing information of the autonomous driving may include activating a controller correcting parameter based on an input or output error of the lower controller. According to an embodiment, the activating of the controller correcting parameter based on the input or output error of the lower controller may include measuring the input or output error between a required acceleration and an output acceleration for the lower controller, comparing between the input or output error and a critical error, and activating the controller correcting parameter to maintain tracking performance to be within a range of the critical error, when the input or output error exceeds the critical error, as the comparison result. According to an embodiment, the correcting of the initial longitudinal control value based on the sensing information of the autonomous driving may include determining a trailer correcting parameter based on whether a trailer is attached to the autonomous driving vehicle.”,
Supplemental Note: the system is able to determine the required acceleration based on a trailer attached to the vehicle and therefore able to determine a correcting parameter. The preset normal pattern is interpreted as the required acceleration and output acceleration being the same, which is evaluated for any parameters needed to be corrected. Overloading is interpreted as applying a correction parameter to the acceleration of the vehicle)
determine whether to maintain the activated autonomous driving (Kang: Col. 7, lines 24 – 32: “The cognition part 222 may recognize a lane based on sensing information from the Radar or Lidar 212 and image information captured by the external camera 213, and may identify a vehicle travelling around a host vehicle, or an obstacle or pedestrian around the host vehicle. In addition, the cognition part 222 may determine whether the trailer is mounted, based on the sensing information from the Radar or Lidar 212 and image information photographed by the external camera 213.”; Col. 7, lines 63 – 67: “The controller 223 may determine whether control needs to be transferred from the system to the driver, based on internal and external states of the vehicle depending on the cognition result of the cognition part 222, and whether the driver inputs a button to release autonomous driving.”)
In sum, Kang teaches an autonomous vehicle comprising a processor, wherein the processor is configured to: acquire a signal indicative of connection between the autonomous vehicle and a trailer; activate an autonomous driving based on the acquired signal; learn a speed-specific acceleration pattern of the autonomous vehicle based on the activated autonomous driving; determine overloading by comparing and analyzing the learned acceleration pattern and a preset normal pattern; and determine whether to maintain the activated autonomous driving. Kang however, does not teach based on a result of the determination on the overloading whereas Smalley does.
Smalley teaches based on a result of the determination on the overloading. (Smalley: Paragraph 0085: “For example, in one or more particular arrangements, the at least one stability evaluation parameter may include a lateral acceleration of the vehicle. A “measured lateral acceleration” a.sub.M of the vehicle 100 may be a lateral acceleration obtained from a sensor (such as lateral acceleration sensor 151) configured to measure or calculate a lateral acceleration of the vehicle, or a lateral acceleration computed using data from a sensor providing information necessary for such a computation.”; Paragraph 0099: “In particular arrangements using yaw rate and lateral acceleration as stability evaluation parameters, “instability” in the vehicle-trailer system may be determined to occur when the total amount of time that the measured lateral acceleration a.sub.M satisfied a predetermined lateral acceleration condition and/or the total amount of time that the measured yaw rate ω.sub.M satisfied a predetermined yaw rate condition exceeds the significant proportion tLIM of the predetermined time period tM. The stability evaluation module 213 may be configured to control operation of the vehicle 100 to generate a stability alert responsive to detection of an instability condition.”; Paragraph 0161: “Unbalanced or overloaded trailers may then produce instability conditions that are detected by the stability evaluation module, prompting generation of alerts and other responses (such as deactivation of a lane tracing assist (LTA) system, for example).”,
Supplemental Note: the overloaded condition causes instability identified by instabilities in lateral acceleration)
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Kang with the teachings of Smalley with a reasonable expectation of success. Please refer to the rejection of claim 1 as both claim the same function and therefore rejected under the same pretenses.
Regarding claim 9, Kang, as modified, teaches provide information about the deactivated autonomous driving through a display. (Kang: Col. 8, lines 1 – 5: “The controller 223 may perform a control operation to output a specific warning notification for requesting for transferring the control to the driver, when the control needs to be transferred to the driver.”)
In sum, Kang teaches provide information about the deactivated autonomous driving through a display. Kang however, does not teach wherein the processor is further configured to: when the overloading is determined, deactivate the autonomous driving whereas Smalley does.
Smalley teaches wherein the processor is further configured to:
when the overloading is determined, (Smalley: Paragraph 0085: “For example, in one or more particular arrangements, the at least one stability evaluation parameter may include a lateral acceleration of the vehicle. A “measured lateral acceleration” a.sub.M of the vehicle 100 may be a lateral acceleration obtained from a sensor (such as lateral acceleration sensor 151) configured to measure or calculate a lateral acceleration of the vehicle, or a lateral acceleration computed using data from a sensor providing information necessary for such a computation.”; Paragraph 0099: “In particular arrangements using yaw rate and lateral acceleration as stability evaluation parameters, “instability” in the vehicle-trailer system may be determined to occur when the total amount of time that the measured lateral acceleration a.sub.M satisfied a predetermined lateral acceleration condition and/or the total amount of time that the measured yaw rate ω.sub.M satisfied a predetermined yaw rate condition exceeds the significant proportion tLIM of the predetermined time period tM. The stability evaluation module 213 may be configured to control operation of the vehicle 100 to generate a stability alert responsive to detection of an instability condition.”; Paragraph 0161: “Unbalanced or overloaded trailers may then produce instability conditions that are detected by the stability evaluation module, prompting generation of alerts and other responses (such as deactivation of a lane tracing assist (LTA) system, for example).”,
Supplemental Note: the overloaded condition causes instability identified by instabilities in lateral acceleration)
deactivate the autonomous driving, and (Smalley: Paragraph 0066: “The stability evaluation module 213 may be configured as described herein to evaluate the results of these computations to determine if an instability condition exists in the vehicle-trailer system. The stability evaluation module 213 may also be configured as described herein to, responsive to a determination that an instability condition exists, autonomously control certain operations of the vehicle 100 to generate instability alerts to users of the vehicle and/or to deactivate certain vehicle systems to facilitate full manual control of the vehicle. These steps may help the user stabilize the motion of the vehicle-trailer system as soon as possible.”)
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Kang with the teachings of Smalley with a reasonable expectation of success. Please refer to the rejection of claim 2 as both claim the same function and therefore rejected under the same pretenses.
Regarding claim 10, Kang, as modified, teaches wherein the processor is configured to:
when no overloading is determined, maintain the activated autonomous driving. (Kang: Col. 10, lines 34 – 49 : “When the trailer is attached as the determination result, the first parameter correcting part 332 may calculate the specification of the trailer based on the sensing information on the autonomous driving (S630). In this case, the specification of the trailer may include information on the length, the volume, and the weight of the trailer, but is not limited thereto. For example, the specification of the trailer may include the shape and the type of the trailer. The first parameter correcting part 332 may determine a trailer correcting parameter P.sub.trail which is calculated by making reference to a preset mapping table of the trailer and corresponds to the specification of the trailer (S640). The trailer mapping table may have the trailer correcting parameter P.sub.trail corresponding to the specification of the trailer and previously defined.”; Col. 11, lines 31 – 57: “According to the present disclosure, the autonomous driving control apparatus 300 may perform a control operation to track the behavior corresponding to a required input value “a_req” required to the real vehicle by applying, to the initial input value “a_raw”, three parameters, which is the controller correcting parameter P.sub.hw, the trailer correcting parameter P.sub.trail, and the driving context correcting parameter P.sub.env, calculated by the control parameter application part 330 and correcting the initial input value “a_raw” as in following equation 1.
a_req=a_raw(P.sub.hw+P.sub.trail+P.sub.env) Equation 1
In addition, according to the present disclosure, the autonomous driving accident may be previously prevented through the strategy to optimize the longitudinal control tracking performance. In addition, the present disclosure may provide a method and an apparatus for controlling autonomous driving, capable of improving the autonomous driving performance in level 3. In addition, according to the present disclosure, the longitudinal control correcting parameter is adaptively applied through the parameter mapping table based on various vehicle specifications and various driving contexts, thereby improving the longitudinal control tracking performance and minimizing maintenance cost.”,
Supplemental Note: the autonomous driving can identify trailer properties to be used to optimize the vehicle’s performance. The autonomous driving in the cited system is able to perform in conditions interpreted as overloading and conditions operating normally. For example, a_req=a_raw if there are no other correction parameters detected)
Regarding claim 11, Kang, as modified, teaches wherein the preset normal pattern comprises a first normal pattern and a second normal pattern, and (Kang: Col. 3, lines 62 – 67: “According to an embodiment, the corrected control value may be calculated by applying at least one of the controller correcting parameter, the trailer correcting parameter, or the driving context correcting parameter to the initial longitudinal control value.”: Col. 7, lines 29 – 32: “In addition, the cognition part 222 may determine whether the trailer is mounted, based on the sensing information from the Radar or Lidar 212 and image information photographed by the external camera 213.”: Col. 8, lines 47 – 63: “The sensing part 310 may include various sensors for autonomous driving and may generate sensing information on the autonomous driving, based on sensing information collected from the sensors. The sensing information on the autonomous driving may include positioning information, precision map information, camera capturing information, the sensing information by the Radar or Lidar, weather information, driving speed information, information on the recognized driver gaze, sensing information on vehicle internal failure, information on a button input to release the autonomous driving, sensing information on the operation of the steering wheel, or the sensing information on the operation of the acceleration or deceleration pedal, but is not limited thereto. The longitudinal control value generator 320 may generate an initial longitudinal control value “a_raw” based on the sensing information on the autonomous driving.”,
Supplemental Note: the preset normal patterns are interpreted as the initial acceleration patterns detected by the sensing part. This can include a vehicle with or without a trailer which both have an “a_raw” value, thus two normal patterns which are set per the initial conditions acquired by the sensing part)
… are connected based on the signal. (Kang: Col. 2, lines 24 – 30: “According to an embodiment, the determining of the trailer correcting parameter based on whether the trailer is attached to the autonomous driving vehicle may include determining whether the trailer is attached, by analyzing the sensing information on the autonomous driving, calculating a specification of the trailer based on the sensing information on the autonomous driving, when the trailer is attached”: Col. 7, lines 24 – 32: “The cognition part 222 may recognize a lane based on sensing information from the Radar or Lidar 212 and image information captured by the external camera 213, and may identify a vehicle travelling around a host vehicle, or an obstacle or pedestrian around the host vehicle. In addition, the cognition part 222 may determine whether the trailer is mounted, based on the sensing information from the Radar or Lidar 212 and image information photographed by the external camera 213.”)
In sum, Kang teaches wherein the preset normal pattern comprises a first normal pattern and a second normal pattern, and the autonomous vehicle and the trailer are determined as being connected based on the signal. Kang however does not fully teach wherein the processor is configured to determine the overloading based on the first normal pattern when the autonomous vehicle and the trailer whereas Smalley does.
Smalley teaches wherein the processor is configured to determine the overloading based on the first normal pattern when the autonomous vehicle and the trailer (Smalley: Paragraph 0017: “Embodiments described herein relate to a system for detecting an instability condition in a moving vehicle-trailer system. The stability monitoring-system includes a processor and a memory communicably coupled to the processor and storing a stability evaluation module including computer-readable instructions that when executed by the processor cause the processor to determine if a measured lateral acceleration of the vehicle satisfies a predetermined lateral acceleration condition and/or a measured yaw rate of the vehicle satisfies a predetermined yaw rate condition. The presence of either (or both of) a predetermined lateral acceleration condition and/or a predetermined yaw rate condition may indicate an instability condition in the vehicle-trailer system. If the measured lateral acceleration satisfies a predetermined lateral acceleration condition, the stability evaluation module may determine a total amount of time within a predetermined time period that the measured lateral acceleration satisfies the predetermined lateral acceleration condition. If the measured yaw rate satisfies a predetermined yaw rate condition, the stability evaluation module may determine a total amount of time within a predetermined time period that the measured yaw rate satisfies the predetermined yaw rate condition.”; Paragraph 0161: “Testing using embodiments of the stability evaluation module described herein has indicated that the stability/instability determinations provided by the module are relatively insensitive to variations in the unloaded trailer parameters, and depend primarily on towing vehicle parameters and parameters of the trailer in a loaded condition. Unbalanced or overloaded trailers may then produce instability conditions that are detected by the stability evaluation module, prompting generation of alerts and other responses (such as deactivation of a lane tracing assist (LTA) system, for example).”,
Supplemental Note: based on the different parameters, including lateral acceleration, the system is able to determine if a predetermined condition is present to cause instability. The conditions without any instability issues and normal lateral acceleration with the trailer are interpreted as the first normal pattern. Overloaded vehicles are taught to produce instability)
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Kang with the teachings of Smalley with a reasonable expectation of success. Please refer to the rejection of claim 4 as both claim the same function and therefore rejected under the same pretenses.
Regarding claim 12, Kang, as modified, teaches wherein the processor is further configured to determine the overloading based on the second normal pattern when the autonomous vehicle and the trailer are not connected based on the signal. (Kang: Col. 10, lines 34 – 49 : “When the trailer is attached as the determination result, the first parameter correcting part 332 may calculate the specification of the trailer based on the sensing information on the autonomous driving (S630). In this case, the specification of the trailer may include information on the length, the volume, and the weight of the trailer, but is not limited thereto. For example, the specification of the trailer may include the shape and the type of the trailer. The first parameter correcting part 332 may determine a trailer correcting parameter P.sub.trail which is calculated by making reference to a preset mapping table of the trailer and corresponds to the specification of the trailer (S640). The trailer mapping table may have the trailer correcting parameter P.sub.trail corresponding to the specification of the trailer and previously defined.”; Col. 11, lines 31 – 57: “According to the present disclosure, the autonomous driving control apparatus 300 may perform a control operation to track the behavior corresponding to a required input value “a_req” required to the real vehicle by applying, to the initial input value “a_raw”, three parameters, which is the controller correcting parameter P.sub.hw, the trailer correcting parameter P.sub.trail, and the driving context correcting parameter P.sub.env, calculated by the control parameter application part 330 and correcting the initial input value “a_raw” as in following equation 1.
a_req=a_raw(P.sub.hw+P.sub.trail+P.sub.env) Equation 1
In addition, according to the present disclosure, the autonomous driving accident may be previously prevented through the strategy to optimize the longitudinal control tracking performance. In addition, the present disclosure may provide a method and an apparatus for controlling autonomous driving, capable of improving the autonomous driving performance in level 3. In addition, according to the present disclosure, the longitudinal control correcting parameter is adaptively applied through the parameter mapping table based on various vehicle specifications and various driving contexts, thereby improving the longitudinal control tracking performance and minimizing maintenance cost.”,
Supplemental Note: the autonomous driving can identify trailer properties to be used to optimize the vehicle’s performance. The autonomous driving in the cited system is able to perform in conditions interpreted as overloading and conditions operating normally. For example, a_req=a_raw if there are no other correction parameters detected such as the trailer not being attached and no need for a P.sub.trail correction parameter)
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Kang et al. (US 11667307 B2) in view of Smalley et al. (US 20250074440 A1) as applied to claim 1 above, and further in view of Jang et al. (US 20230398983 A1).
Regarding claim 6, Kang, as modified, does not teach wherein the autonomous driving comprises a smart cruise control (SCC) whereas Lee does.
Jang teaches wherein the autonomous driving comprises a smart cruise control (SCC). (Jang: Paragraph 0053: “The autonomous driving system 100 may provide various functions to the driver. For example, the autonomous driving system 100 may provide functions such as lane departure warning (LDW), lane keeping assist (LKA), high beam assist (HBA), automatic emergency braking (AEB), traffic sign recognition (TSR), smart cruise control (SCC), and/or blind spot detection (BSD).”).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Kang with the teachings of Lee with a reasonable expectation of success. Autonomous driving is taught by Kang to be between automation levels 0 to 5 per the American Society of Automotive Engineers (Kang: Col. 5, lines 57 – 67), which the vehicle system is able to be configured to. SCC as taught by Jang is also an autonomous driving function, therefore the SCC would be a simple substitution with the autonomous driving function of Kang’s vehicle by one of ordinary skill in the art.
Claim 13 is rejected under 35 U.S.C. 103 as being unpatentable over Kang et al. (US 11667307 B2) in view of Smalley et al. (US 20250074440 A1) as applied to claim 8 above, and further in view of Jang et al. (US 20230398983 A1).
Regarding claim 13, Kang, as modified, does not teach wherein the autonomous driving comprises a smart cruise control (SCC) whereas Jang does.
Jang teaches wherein the autonomous driving comprises a smart cruise control (SCC). (Jang: Paragraph 0053: “The autonomous driving system 100 may provide various functions to the driver. For example, the autonomous driving system 100 may provide functions such as lane departure warning (LDW), lane keeping assist (LKA), high beam assist (HBA), automatic emergency braking (AEB), traffic sign recognition (TSR), smart cruise control (SCC), and/or blind spot detection (BSD).”).
Therefore, it would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to have been modified the invention disclosed by Kang with the teachings of Lee with a reasonable expectation of success. Please refer to the rejection of claim 6 as both claim the same function and therefore rejected under the same pretenses.
Conclusion
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/SHIVAM SHARMA/Examiner, Art Unit 3665
/Erin D Bishop/Supervisory Patent Examiner, Art Unit 3665