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 .
Response to Amendment
The amendment filed on 03/10/2025 have been entered. Claims 1-3,5-12,14-19,21-29,31 and 33-39 are remaining pending in the application. Claims 4, 13, 20, 30 and 32 are cancelled and claims 36-39 are the newly added dependent claims.
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 1-3, 6-7, 9, 21-23 and 35 are rejected under 35 U.S.C. 103 as being unpatentable over Patil et al. US 20180082493, in view of Luo et al. US 20190120964, in view of Stenneth et al. US 20220309521 and further in view of Taguchi Seiki JP 2005346333.
Regarding claim 1, Patil et al. teach A method for providing detected object data, comprising: detecting a plurality of objects proximate to a vehicle; (Patil et al. US 20180082493 abstract; paragraph [0003]-[0006]; [0080]-[0086]; [0088]-[0095]; figures 1-9;)
As illustrated in FIG. 8, V2V systems may provide improved active safety by providing local sensor data collection abilities as well as communication options to communicate with other devices in order to share and receive additional information such as sensor information, identification information, suggested action information, hysteresis information, or other forms of information. For example, vehicle 802 may be provided with sensors and may also be equipped to obtain sensor information from other vehicles 804-812 or other devices or elements in the area such as a pedestrian using V2P and/or a traffic signal element using V2I (Patil et al. par. 82). Self-driving cars are an exciting area of innovation these days. One way to help self-driving cars is using V2V communication. FIG. 8 illustrates examples of various sensors that may be used for self-driving, including Lidar, radar, and cameras. Because these sensors are line of sight, the sensors have limitations in the amount of information the sensors may collect and then provide. V2V communication, on the other hand, is not line of sight and can work for non-line of sight cases also. This can be particularly helpful for the case where two vehicles are approaching intersections. V2V communication can be used to share sensor information between vehicles (Patil et al. par. 84).
According to the cited passages and figures, examiner interpret the vehicle 802 obtain the proximity road information around the vehicle via various of sensor. The vehicle 802 share the information through V2V communication with those vehicles in the proximity range of communication like those vehicles 804, 808, 810 and 812 as illustrated in the figure 8.
generating a sensor data sharing message including host data and detected object data based on the information associated with the plurality of detected objects proximate to the vehicle, wherein the detected object data is organized based on the plurality of data groups,
As illustrated in FIG. 8, V2V systems may provide improved active safety by providing local sensor data collection abilities as well as communication options to communicate with other devices in order to share and receive additional information such as sensor information, identification information, suggested action information, hysteresis information, or other forms of information. For example, vehicle 802 may be provided with sensors and may also be equipped to obtain sensor information from other vehicles 804-812 or other devices or elements in the area such as a pedestrian using V2P and/or a traffic signal element using V2I (Patil et al. par. 82).
According to the cited passages and figures, examiner interpret identification information as the indexing information corresponding with one or more data groups like V2P (vehicle-to-pedestrian). Examiner interpret the pedestrian (V2P) in the proximity to the host vehicle (V2V) as one group and V2I (vehicle-to-infrastructure) like traffic light in the proximity to the host vehicle (V2V) as another group.
and transmitting the sensor data sharing message.
It may be generally expected that vehicles that are in proximity will send basic safety messages (BSMs) to each other. Such BSMs may contain location of the vehicle along with expected trajectory. If such BSMs can also contain information from sensors being carried by the vehicles, then any given vehicle can receive such information and decide whether it is the closest to an area, among all the vehicles for which it has received the messages. Further, according to one or more examples, the one or more metrics may be derived based on basic safety messages (BSMs) transmitted by the one or more of the vehicles. In some cases, the one or more vehicles may transmit their sensor capability along with BSMs (Patil et al. par. 93).
However, Patil et al. do teach a plurality of the object in the proximate to the vehicle but Patil et al. do not explicitly teach organizing information associated with the plurality of detected objects proximate to the vehicle into a plurality of data groups based at least in part on a plurality of respective locations of the plurality of detected objects, wherein the plurality of data groups are associated with one or more factors that increase or decrease a risk of collision with the vehicle; the detected object data of each of the data groups is associated with respective indexing information of a first array of index values, and the host data includes the indexing information of the first array of index values.
Luo et al. teach organizing information associated with the plurality of detected objects proximate to the vehicle into a plurality of data groups based at least in part on a plurality of respective locations of the plurality of detected objects, (Luo et al. US 20190120964 abstract; paragraph [0017]; [0026]-[0037]; [0061]-[0063]; figures 1-7;)
The disclosure provides for an in-vehicle computing system of a vehicle, the in-vehicle computing system including a sensor subsystem in communication with an optical sensor, a processor, and memory storing instructions executable by the processor to instruct the optical sensor to scan an assigned region around the vehicle, receive, from the optical sensor, locally scanned data corresponding to the assigned region, process the locally scanned data to build a first portion of a three-dimensional map of an environment of the vehicle, transmit the processed scanned data to at least one other vehicle, receive additional map data from the at least one other vehicle, and build a second, different portion of the three-dimensional map using the received additional map data. In a first example of the in-vehicle computing system, the optical sensor may additionally or alternatively include a Light Detection and Ranging (LiDAR) sensor system and the locally scanned data additionally or alternatively includes point-cloud data corresponding to the assigned region. A second example of the in-vehicle computing system optionally includes the first example, and further includes the in-vehicle computing system, wherein the vehicle forms a group with the at least one other vehicle based on a proximity of the at least one other vehicle. A third example of the in-vehicle computing system optionally includes one or both of the first example and the second example, and further includes the in-vehicle computing system, wherein the instructions are further executable to receive an assignment to scan the assigned region from another vehicle of the group, each vehicle of the group being assigned a different region of a collaborative scanning area around the group, the three-dimensional map corresponding to the collaborative scanning area…………….. A seventh example of the in-vehicle computing system optionally includes one or more of the first through the sixth examples, and further includes the in-vehicle computing system, wherein the instructions are further executable to process the locally scanned data by converting the locally scanned data to a range image, obtaining a previously generated three-dimensional road map associated with the environment of the vehicle, subtracting static portions of the range image determined to be located on a surface of the three-dimensional road map, and processing only the non-subtracted portions of the range image to build the first portion of the three-dimensional map (Luo et al. par. 61).
According to the cited passages and figures, examiner interpret forms a group with at least one other vehicle based on a proximity of the at least one other vehicle and each vehicle assign to difference region for scanning the area to detect objects. Those objects is assigning as the organizing information associate the vehicle in the group to provide the plurality of detected objects information respective to the plurality of the locations.
Therefore, It would have been obviously to one of ordinary skill in the art before the effective filing date of the claim invention to combine Patil et al. and Luo et al. by comprising the teaching of Luo et al. into the method of Patil et al.. The motivation to combine these arts is to assign the group for the vehicles in the proximity distance to each other and each of vehicle in the group can assign a difference region to detect the object or obstacle of the environment surrounding the vehicle from Luo et al. reference into Patil et al. reference to cover the large environment to enhance the traffic safety.
However, the combination of Patil et al. and Luo et al. do teach the index information but the combination of Patil et al. and Luo et al. do not explicitly teach wherein the plurality of data groups are associated with one or more factors that increase or decrease a risk of collision with the vehicle; the detected object data of each of the data groups is associated with respective indexing information of a first array of index values, and the host data includes the indexing information of the first array of index values.
Stenneth et al. teach the detected object data of each of the data groups is associated with respective indexing information of a first array of index values, and the host data includes the indexing information of the first array of index values; (Stenneth et al. US 20220309521 abstract; paragraphs [0005]-[0007]; [0017]-[0020]; [0022]-[0026]; [0028]-[0031]; [0033]; [0049]; [0069]; [0122]; [0125]-[0128]; figures 1-8;)
The distance data detection sensor may include a laser range finder that rotates a mirror directing a laser to the surroundings or vicinity of the collection vehicle on a roadway or another collection device on any type of pathway. A connected vehicle includes a communication device and an environment sensor array for detecting and reporting the surroundings of the shared vehicle 124 to the mapping system 121. The connected vehicle may include an integrated communication device coupled with an in-dash navigation system. The connected vehicle may include an ad-hoc communication device such as a mobile device or smartphone in communication with a vehicle system. The communication device connects the vehicle to a network 127 including at least the mapping system 121. The network 127 may be the Internet or connected to the internet (Stenneth et al. par. 25). Vision-based techniques are used by the device 122 to acquire information about candidates 135 in the area around the shared vehicle 124. Video data, image data, or other sensor data may be collected and processed to identify features or attributes of a candidate 135. Image recognition methods or classifiers such as neural networks may be used. The collected information may be processed and transformed into Boolean values or scores that are input into the computation for the interest index value for a candidate 135 at a particular time. The shared vehicle 124 identifies a search radius that is considered for collecting parameters to be used for the interest index computation. The search radius may be dynamic and may depend on different features or variables of the location such as pedestrian density, street type, vehicle orientation, weather, etc. When the search function (e.g., looking for a passenger) is active, the device 122 monitors candidates 135 that are inside the search radius. The device 122 may collect information about the candidate 135 using respective sensors such as cameras, radar, LIDAR, accelerometers, gyroscopes, GPS, ultrasonic sensors, etc. The collected information may be used to determine Boolean values or scores for parameters such as the candidate's context (alone, in group, carrying something, etc.), a candidate's walking maneuvers and the possible detour made to go closer to that vehicle (accelerometer, gyroscope, GPS), the proximity of the vehicle (GPS, radar, LIDAR, Ultrasonic sensors), eye contact with the vehicle (based on cameras), facial expression detection (based on cameras), a possible reaction when the vehicle communicates with the candidate 135 (based on cameras), the presence of other “bookable” vehicles in the direct vicinity (i.e. “competitor vehicles), and environmental attributes (Weather, etc.) among others. Information may be collected by the device 122 constantly or at set intervals (Stenneth et al. par. 28).
According to the cited passages and figures, examiner interpret the feature or attribute of pedestrian 135 as an interest index value in the proximity with the vehicle which had been detect by an environment sensor array to monitor the objects surround the vehicle.
Therefore, It would have been obviously to one of ordinary skill in the art before the effective filing date of the claim invention to combine Patil et al. and Luo et al. with Stenneth et al. by comprising the teaching of Stenneth et al. into the method of Patil et al. and Luo et al.. The motivation to combine these arts is to provide a simple substitution of index value for pedestrian from Stenneth et al. reference into Patil et al. and Luo et al. so the system can utilize the index value for determine the object in the proximity to the vehicle within the radius parameter to avoid collision.
The combination of Patil et al., Luo et al. and Stenneth et al. do not explicitly teach wherein the plurality of data groups are associated with one or more factors that increase or decrease a risk of collision with the vehicle.
Taguchi Seiki teaches wherein the plurality of data groups are associated with one or more factors that increase or decrease a risk of collision with the vehicle; (Taguchi Seiki JP 2005346333 paragraphs [0006]-[0011]; [0025]-[0026]; [0028]-[0032]; [0038]-[0039]; [0071]; [0174]-[0148];)
The invention described in claim 1 is characterized by a communication device having a transmission means for periodically transmitting specified data requesting the transmission of response data, a factor detection means for detecting factors that increase or decrease the risk of a collision of the vehicle, and a frequency change means for changing the frequency of transmitting the specified data based on the factors detected by the factor detection means (Taguchi Seiki par. 6).
Therefore, It would have been obviously to one of ordinary skill in the art before the effective filing date of the claim invention to combine Patil et al., Luo et al. and Stenneth et al. with Taguchi Seiki by comprising the teaching of Taguchi Seiki into the method of Patil et al., Luo et al. and Stenneth et al.. The motivation to combine these arts is to provide a simple substitution of a factor detection means for detecting factors that increase or decrease the risk of a collision of the vehicle from Taguchi Seiki reference into Patil et al., Luo et al. and Stenneth et al. so the results of the substitution would have been predictable whether the risk of vehicle accident will increase or decrease.
Regarding claim 2, the combination of Patil et al., Luo et al., Stenneth et al. and Taguchi Seiki disclose The method of claim 1 wherein the plurality of data groups define an area around the vehicle.
Once grouped, the combined scanning processes of the vehicles may cover a viewing range 106 representing a 360 degree area around the group of vehicles that may be processed for display as a three-dimensional map of the environment of the vehicles. In the illustrated example, the scanning and processing tasks are distributed such that vehicle 104b is responsible for scanning and processing LiDAR data in area 106a, vehicle 104c is responsible for scanning and processing LiDAR data in area 106b, and vehicle 104d is responsible for scanning and processing LiDAR data in area 106c. When combined, areas 106a-c cover the full viewing range 106 (Luo et al. par. 18). One way for grouping to be performed is to determine a group of k nearest neighbors, where k is a non-zero positive integer. For example, a group of 3 nearest neighbors may be formed using received discovery information. Additional information may be used for grouping vehicles. For example, locations and directions of vehicles on roads, as well as predicted routes of vehicles on roads, may be used to divide vehicles into several groups. In some examples, groups may be formed to remain unchanged for a threshold amount of time (e.g., vehicles travelling in the same direction may be grouped, as the likelihood that a vehicle in the group will become spaced from other vehicles in the group by a threshold distance may be low). In contrast, vehicles travelling in opposite directions may not be grouped, as the vehicles may only be within the threshold distance for a very short amount of time (Luo et al. par. 20).
Regarding claim 3, the combination of Patil et al., Luo et al., Stenneth et al. and Taguchi Seiki disclose The method of claim 2 wherein the area is a circle around the vehicle, and the host data includes a first information element to define a radius of the circle.
Each vehicle uses a predefined search radius for a location that is considered for collecting data to be used for the interest index computation. The predefined search radius may be defined based on the capabilities of the shared vehicle 124 (sensor quality/range) and/or the context of the location. The data may be constantly collected. Alternatively, some data may be collected at certain intervals, e.g., periodically every 1, 2, 5, 10 seconds, etc. The shared vehicle 124 may be equipped and/or may communicate with one or more sensors configured to acquire information about a candidate 135. For example, the shared vehicle 124 may be equipped with cameras, a Radar system, and/or a LiDAR system. The shared vehicle 124 may use cameras to detect attributes of the candidate 135, walking maneuvers and trajectory, proximity of the candidate 135, eye contact or facial expression of the candidate 135, or a reaction after an attempt by the shared vehicle 124 to contact the candidate 135 (Stenneth et al. par. 49).
Regarding claim 6, the combination of Patil et al., Luo et al., Stenneth et al. and Taguchi Seiki disclose The method of claim 1 wherein the plurality of data groups are associated with a heading of each of the plurality of detected objects.
It may be generally expected that vehicles that are in proximity will send basic safety messages (BSMs) to each other. Such BSMs may contain location of the vehicle along with expected trajectory. If such BSMs can also contain information from sensors being carried by the vehicles, then any given vehicle can receive such information and decide whether it is the closest to an area, among all the vehicles for which it has received the messages. Further, according to one or more examples, the one or more metrics may be derived based on basic safety messages (BSMs) transmitted by the one or more of the vehicles. In some cases, the one or more vehicles may transmit their sensor capability along with BSMs (Patil et. al. par. 93).
According to the cited passages and figures examiner interpret vehicle location along with expected trajectory of the vehicle is same as the heading of each of the one or more detect objects which similar to the figure 8 of Patil et. al. reference.
Regarding claim 7, the combination of Patil et al., Luo et al., Stenneth et al. and Taguchi Seiki disclose The method of claim 1 further comprising transmitting a basic safety message including a current location of the vehicle, wherein the sensor data sharing message is associated with the basic safety message.
It may be generally expected that vehicles that are in proximity will send basic safety messages (BSMs) to each other. Such BSMs may contain location of the vehicle along with expected trajectory. If such BSMs can also contain information from sensors being carried by the vehicles, then any given vehicle can receive such information and decide whether it is the closest to an area, among all the vehicles for which it has received the messages. Further, according to one or more examples, the one or more metrics may be derived based on basic safety messages (BSMs) transmitted by the one or more of the vehicles. In some cases, the one or more vehicles may transmit their sensor capability along with BSMs (Patil et. al. par. 93).
Regarding claim 9, the combination of Patil et al., Luo et al., Stenneth et al. and Taguchi Seiki disclose The method of claim 1 wherein detecting the plurality of objects proximate to the vehicle is based on signals obtained by a radar sensor, a lidar sensor, an optical sensor, radio frequency communication systems, or combinations thereof.
FIG. 8 illustrates examples of various sensors that may be used for self-driving, including Lidar, radar, and cameras. Because these sensors are line of sight, the sensors have limitations in the amount of information the sensors may collect and then provide. V2V communication, on the other hand, is not line of sight and can work for non-line of sight cases also. This can be particularly helpful for the case where two vehicles are approaching intersections. V2V communication can be used to share sensor information between vehicles (Patil et. al. par. 84).
Regarding claim 21, Patil et al. teach An apparatus, comprising: a memory; at least one transceiver; at least one processor communicatively coupled to the memory and the at least one transceiver, and configured to: detect a plurality of objects proximate to a vehicle; (Patil et al. US 20180082493 abstract; paragraph [0003]-[0006]; [0008]-[0010]; [0066]-[0070] [0080]-[0086]; [0088]-[0095]; [0106]-[0108]; figures 1-9; )
Certain aspects of the present disclosure provide a method that may be performed, for example, by a communication device integrated in or installed on a vehicle. The method generally includes determining, based a given location, a set of frequency resources to be used for transmitting sensor information obtained at one or more vehicles for that given location, and utilizing the set of frequency resources to transmit sensor information or monitor for sensor information (Patil et al. par. 9). For example, the wireless device 502 may be configured to perform operations 900 illustrated in FIG. 9 as well as other operations described herein (Patil et al. par. 66). The wireless device 502 may include a processor 504 that controls operation of the wireless device 502. The processor 504 may also be referred to as a central processing unit (CPU). Memory 506, which may include both read-only memory (ROM) and random access memory (RAM), provides instructions and data to the processor 504 (Patil et al. par. 67). The wireless device 502 may also include a housing 508 that may include a transmitter 510 and a receiver 512 to allow transmission and reception of data between the wireless device 502 and a remote location. The transmitter 510 and receiver 512 may be combined into a transceiver 514 (Patil et al. par. 68) . As illustrated in FIG. 8, V2V systems may provide improved active safety by providing local sensor data collection abilities as well as communication options to communicate with other devices in order to share and receive additional information such as sensor information, identification information, suggested action information, hysteresis information, or other forms of information. For example, vehicle 802 may be provided with sensors and may also be equipped to obtain sensor information from other vehicles 804-812 or other devices or elements in the area such as a pedestrian using V2P and/or a traffic signal element using V2I (Patil et al. par. 82). Self-driving cars are an exciting area of innovation these days. One way to help self-driving cars is using V2V communication. FIG. 8 illustrates examples of various sensors that may be used for self-driving, including Lidar, radar, and cameras. Because these sensors are line of sight, the sensors have limitations in the amount of information the sensors may collect and then provide. V2V communication, on the other hand, is not line of sight and can work for non-line of sight cases also. This can be particularly helpful for the case where two vehicles are approaching intersections. V2V communication can be used to share sensor information between vehicles (Patil et al. par. 84).
According to the cited passages and figures, examiner interpret the vehicle 802 obtain the proximity road information around the vehicle via various of sensor. The vehicle 802 share the information through V2V communication with those vehicles in the proximity range of communication like those vehicles 804, 808, 810 and 812 as illustrated in the figure 8.
generate a sensor data sharing message including host data and detected object data based on the information associated with the plurality of detected objects proximate to the vehicle, wherein the detected object data is organized based on the plurality of data groups,
As illustrated in FIG. 8, V2V systems may provide improved active safety by providing local sensor data collection abilities as well as communication options to communicate with other devices in order to share and receive additional information such as sensor information, identification information, suggested action information, hysteresis information, or other forms of information. For example, vehicle 802 may be provided with sensors and may also be equipped to obtain sensor information from other vehicles 804-812 or other devices or elements in the area such as a pedestrian using V2P and/or a traffic signal element using V2I (Patil et al. par. 82).
According to the cited passages and figures, examiner interpret identification information as the indexing information corresponding with one or more data groups like V2P (vehicle-to-pedestrian). Examiner interpret the pedestrian (V2P) in the proximity to the host vehicle (V2V) as one group and V2I (vehicle-to-infrastructure) like traffic light in the proximity to the host vehicle (V2V) as another group.
However, Patil et al. do teach a plurality of the object in the proximate to the vehicle but Patil et al. do not explicitly teach organize information associated with the plurality of detected objects proximate to the vehicle into a plurality of data groups based at least in part on one or more respective locations of the plurality of detected objects, wherein the plurality of data groups are associated with one or more factors that increase or decrease a risk of collision with the vehicle; the detected object data of each of the data groups is associated with respective indexing information of an array of index values, and the host data includes the indexing information of the array of index values; and transmit the sensor data sharing message.
Luo et al. teach organize information associated with the plurality of detected objects proximate to the vehicle into a plurality of data groups based at least in part on one or more respective locations of the plurality of detected objects, (Luo et al. US 20190120964 abstract; paragraph [0017]-[0020]; [0026]-[0037]; [0061]-[0063]; figures 1-7;)
One way for grouping to be performed is to determine a group of k nearest neighbors, where k is a non-zero positive integer. For example, a group of 3 nearest neighbors may be formed using received discovery information. Additional information may be used for grouping vehicles. For example, locations and directions of vehicles on roads, as well as predicted routes of vehicles on roads, may be used to divide vehicles into several groups. In some examples, groups may be formed to remain unchanged for a threshold amount of time (e.g., vehicles travelling in the same direction may be grouped, as the likelihood that a vehicle in the group will become spaced from other vehicles in the group by a threshold distance may be low). In contrast, vehicles travelling in opposite directions may not be grouped, as the vehicles may only be within the threshold distance for a very short amount of time (Luo et al. par. 20). The disclosure provides for an in-vehicle computing system of a vehicle, the in-vehicle computing system including a sensor subsystem in communication with an optical sensor, a processor, and memory storing instructions executable by the processor to instruct the optical sensor to scan an assigned region around the vehicle, receive, from the optical sensor, locally scanned data corresponding to the assigned region, process the locally scanned data to build a first portion of a three-dimensional map of an environment of the vehicle, transmit the processed scanned data to at least one other vehicle, receive additional map data from the at least one other vehicle, and build a second, different portion of the three-dimensional map using the received additional map data. In a first example of the in-vehicle computing system, the optical sensor may additionally or alternatively include a Light Detection and Ranging (LiDAR) sensor system and the locally scanned data additionally or alternatively includes point-cloud data corresponding to the assigned region. A second example of the in-vehicle computing system optionally includes the first example, and further includes the in-vehicle computing system, wherein the vehicle forms a group with the at least one other vehicle based on a proximity of the at least one other vehicle. A third example of the in-vehicle computing system optionally includes one or both of the first example and the second example, and further includes the in-vehicle computing system, wherein the instructions are further executable to receive an assignment to scan the assigned region from another vehicle of the group, each vehicle of the group being assigned a different region of a collaborative scanning area around the group, the three-dimensional map corresponding to the collaborative scanning area…………….. A seventh example of the in-vehicle computing system optionally includes one or more of the first through the sixth examples, and further includes the in-vehicle computing system, wherein the instructions are further executable to process the locally scanned data by converting the locally scanned data to a range image, obtaining a previously generated three-dimensional road map associated with the environment of the vehicle, subtracting static portions of the range image determined to be located on a surface of the three-dimensional road map, and processing only the non-subtracted portions of the range image to build the first portion of the three-dimensional map (Luo et al. par. 61).
According to the cited passages and figures, examiner interpret forms a group with at least one other vehicle based on a proximity of the at least one other vehicle and each vehicle assign to difference region for scanning the area to detect objects. Those objects is assigning as the organizing information associate the vehicle in the group to provide the plurality of detected objects information respective to the plurality of the locations.
Therefore, It would have been obviously to one of ordinary skill in the art before the effective filing date of the claim invention to combine Patil et al. and Luo et al. by comprising the teaching of Luo et al. into the system of Patil et al.. The motivation to combine these arts is to assign the group for the vehicles in the proximity distance to each other and each of vehicle in the group can assign a difference region to detect the object or obstacle of the environment surrounding the vehicle from Luo et al. reference into Patil et al. reference to cover the large environment to enhance the traffic safety.
However, the combination of Patil et al. and Luo et al. do teach the index information but the combination of Patil et al. and Luo et al. do not explicitly teach wherein the plurality of data groups are associated with one or more factors that increase or decrease a risk of collision with the vehicle; the detected object data of each of the data groups is associated with respective indexing information of an array of index values, and the host data includes the indexing information of the array of index values; and transmit the sensor data sharing message.
Stenneth et al. teach the detected object data of each of the data groups is associated with respective indexing information of an array of index values, and the host data includes the indexing information of the array of index values; and transmit the sensor data sharing message. (Stenneth et al. US 20220309521 abstract; paragraphs [0005]-[0007]; [0017]-[0020]; [0022]-[0026]; [0028]-[0031]; [0033]; [0049]; [0069]; [0122]; [0125]-[0128]; figures 1-8;)
The distance data detection sensor may include a laser range finder that rotates a mirror directing a laser to the surroundings or vicinity of the collection vehicle on a roadway or another collection device on any type of pathway. A connected vehicle includes a communication device and an environment sensor array for detecting and reporting the surroundings of the shared vehicle 124 to the mapping system 121. The connected vehicle may include an integrated communication device coupled with an in-dash navigation system. The connected vehicle may include an ad-hoc communication device such as a mobile device or smartphone in communication with a vehicle system. The communication device connects the vehicle to a network 127 including at least the mapping system 121. The network 127 may be the Internet or connected to the internet (Stenneth et al. par. 25). Vision-based techniques are used by the device 122 to acquire information about candidates 135 in the area around the shared vehicle 124. Video data, image data, or other sensor data may be collected and processed to identify features or attributes of a candidate 135. Image recognition methods or classifiers such as neural networks may be used. The collected information may be processed and transformed into Boolean values or scores that are input into the computation for the interest index value for a candidate 135 at a particular time. The shared vehicle 124 identifies a search radius that is considered for collecting parameters to be used for the interest index computation. The search radius may be dynamic and may depend on different features or variables of the location such as pedestrian density, street type, vehicle orientation, weather, etc. When the search function (e.g., looking for a passenger) is active, the device 122 monitors candidates 135 that are inside the search radius. The device 122 may collect information about the candidate 135 using respective sensors such as cameras, radar, LIDAR, accelerometers, gyroscopes, GPS, ultrasonic sensors, etc. The collected information may be used to determine Boolean values or scores for parameters such as the candidate's context (alone, in group, carrying something, etc.), a candidate's walking maneuvers and the possible detour made to go closer to that vehicle (accelerometer, gyroscope, GPS), the proximity of the vehicle (GPS, radar, LIDAR, Ultrasonic sensors), eye contact with the vehicle (based on cameras), facial expression detection (based on cameras), a possible reaction when the vehicle communicates with the candidate 135 (based on cameras), the presence of other “bookable” vehicles in the direct vicinity (i.e. “competitor vehicles), and environmental attributes (Weather, etc.) among others. Information may be collected by the device 122 constantly or at set intervals (Stenneth et al. par. 28).
According to the cited passages and figures, examiner interpret the feature or attribute of pedestrian 135 as an interest index value in the proximity with the vehicle which had been detect by an environment sensor array to monitor the objects surround the vehicle.
Therefore, It would have been obviously to one of ordinary skill in the art before the effective filing date of the claim invention to combine Patil et al. and Luo et al. with Stenneth et al. by comprising the teaching of Stenneth et al. into the system of Patil et al. and Luo et al.. The motivation to combine these arts is to provide a simple substitution of index value for pedestrian from Stenneth et al. reference into Patil et al. and Luo et al. so the system can utilize the index value for determine the object in the proximity to the vehicle within the radius parameter to avoid collision.
The combination of Patil et al., Luo et al. and Stenneth et al. do not explicitly teach wherein the plurality of data groups are associated with one or more factors that increase or decrease a risk of collision with the vehicle.
Taguchi Seiki teaches wherein the plurality of data groups are associated with one or more factors that increase or decrease a risk of collision with the vehicle; (Taguchi Seiki JP 2005346333 paragraphs [0006]-[0011]; [0025]-[0026]; [0028]-[0032]; [0038]-[0039]; [0071]; [0174]-[0148];)
The invention described in claim 1 is characterized by a communication device having a transmission means for periodically transmitting specified data requesting the transmission of response data, a factor detection means for detecting factors that increase or decrease the risk of a collision of the vehicle, and a frequency change means for changing the frequency of transmitting the specified data based on the factors detected by the factor detection means (Taguchi Seiki par. 6).
Therefore, It would have been obviously to one of ordinary skill in the art before the effective filing date of the claim invention to combine Patil et al., Luo et al. and Stenneth et al. with Taguchi Seiki by comprising the teaching of Taguchi Seiki into the system of Patil et al., Luo et al. and Stenneth et al.. The motivation to combine these arts is to provide a simple substitution of a factor detection means for detecting factors that increase or decrease the risk of a collision of the vehicle from Taguchi Seiki reference into Patil et al., Luo et al. and Stenneth et al. so the results of the substitution would have been predictable whether the risk of vehicle accident will increase or decrease.
Regarding claim 22, the combination of Patil et al., Luo et al., Stenneth et al. and Taguchi Seiki disclose The apparatus of claim 21 wherein the at least one processor is further configured to transmit a basic safety message including a current location of the vehicle, wherein the sensor data sharing message is associated with the basic safety message.
It may be generally expected that vehicles that are in proximity will send basic safety messages (BSMs) to each other. Such BSMs may contain location of the vehicle along with expected trajectory. If such BSMs can also contain information from sensors being carried by the vehicles, then any given vehicle can receive such information and decide whether it is the closest to an area, among all the vehicles for which it has received the messages. Further, according to one or more examples, the one or more metrics may be derived based on basic safety messages (BSMs) transmitted by the one or more of the vehicles. In some cases, the one or more vehicles may transmit their sensor capability along with BSMs (Patil et al. par. 93).
Regarding claim 23, the combination of Patil et al., Luo et al., Stenneth et al. and Taguchi Seiki disclose The apparatus of claim 21 wherein the at least one processor is further configured to detect the plurality of objects proximate to the vehicle based on signals obtained by a radar sensor, a lidar sensor, an optical sensor, radio frequency communication systems, or combinations thereof.
FIG. 8 illustrates examples of various sensors that may be used for self-driving, including Lidar, radar, and cameras. Because these sensors are line of sight, the sensors have limitations in the amount of information the sensors may collect and then provide. V2V communication, on the other hand, is not line of sight and can work for non-line of sight cases also. This can be particularly helpful for the case where two vehicles are approaching intersections. V2V communication can be used to share sensor information between vehicles (Patil et al. par. 84).
Regarding claim 35, the combination of Patil et al., Luo et al., Stenneth et al. and Taguchi Seiki disclose The apparatus of claim 21 wherein the array of index values identifies data locations of the data groups in the detected object data.
The distance data detection sensor may include a laser range finder that rotates a mirror directing a laser to the surroundings or vicinity of the collection vehicle on a roadway or another collection device on any type of pathway. A connected vehicle includes a communication device and an environment sensor array for detecting and reporting the surroundings of the shared vehicle 124 to the mapping system 121. The connected vehicle may include an integrated communication device coupled with an in-dash navigation system. The connected vehicle may include an ad-hoc communication device such as a mobile device or smartphone in communication with a vehicle system. The communication device connects the vehicle to a network 127 including at least the mapping system 121. The network 127 may be the Internet or connected to the internet (Stenneth et al. par. 25). Vision-based techniques are used by the device 122 to acquire information about candidates 135 in the area around the shared vehicle 124. Video data, image data, or other sensor data may be collected and processed to identify features or attributes of a candidate 135. Image recognition methods or classifiers such as neural networks may be used. The collected information may be processed and transformed into Boolean values or scores that are input into the computation for the interest index value for a candidate 135 at a particular time. The shared vehicle 124 identifies a search radius that is considered for collecting parameters to be used for the interest index computation. The search radius may be dynamic and may depend on different features or variables of the location such as pedestrian density, street type, vehicle orientation, weather, etc. When the search function (e.g., looking for a passenger) is active, the device 122 monitors candidates 135 that are inside the search radius. The device 122 may collect information about the candidate 135 using respective sensors such as cameras, radar, LIDAR, accelerometers, gyroscopes, GPS, ultrasonic sensors, etc. The collected information may be used to determine Boolean values or scores for parameters such as the candidate's context (alone, in group, carrying something, etc.), a candidate's walking maneuvers and the possible detour made to go closer to that vehicle (accelerometer, gyroscope, GPS), the proximity of the vehicle (GPS, radar, LIDAR, Ultrasonic sensors), eye contact with the vehicle (based on cameras), facial expression detection (based on cameras), a possible reaction when the vehicle communicates with the candidate 135 (based on cameras), the presence of other “bookable” vehicles in the direct vicinity (i.e. “competitor vehicles), and environmental attributes (Weather, etc.) among others. Information may be collected by the device 122 constantly or at set intervals (Stenneth et al. par. 28).
According to the cited passages and figures, examiner interpret the feature or attribute of pedestrian 135 as an interest index value in the proximity with the vehicle which had been detect by an environment sensor array to monitor the objects surround the vehicle.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Patil et al. US 20180082493, in view of Luo et al. US 20190120964, in view of Stenneth et al. US 20220309521, in view of Taguchi Seiki JP 2005346333 and further in view of Son et al. US 20190035277.
Regarding claim 8, the combination of Patil et al., Luo et al., Stenneth et al. and Taguchi Seiki teach all the limitation in the claim 7.
The combination of Patil et al., Luo et al., Stenneth et al. and Taguchi Seiki do not explicitly teach The method of claim 7 wherein the basic safety message and the sensor data sharing message each include an identification information element containing the same identification value.
Son et al. teach The method of claim 7 wherein the basic safety message and the sensor data sharing message each include an identification information element containing the same identification value. (Son et al. US 20190035277 abstract; paragraph [0006]-[0007]; [0056]-[0060]; [0077]; figures 1-16;)
The electronic device 101 (e.g., the processor 120) included in the vehicle 220 may manage, information (e.g., driving log) such as those shown in FIG. 5B. For example, the information 550 may be categorized into identification information 551, position 552, speed 553, heading 554, or peculiarity 555 and may include data types for at least part of BSMs or PSMs. The electronic device 101 may manage various message set types. As described above, the vehicle 220 may receive a communication signal 511, a communication signal 521, or a communication signal 531 at the first time t1. The communication signal 511 may include identification information V1 about the vehicle 240, the communication signal 521 may include identification information V2 about the vehicle 250, and the communication signal 531 may include identification information P1 about the electronic device 101. For example, the electronic device 101 may also determine that the identification information V2 corresponds to the identification information V3 based on the fact that the speed S2 and heading θ2 corresponding to the identification information V2 are the same as the speed S2 and heading θ2, respectively, corresponding to the identification information V3. In other words, the electronic device 101 may determine the correspondence between the pieces of identification information by using other information than the position of the vehicle. Upon identifying the non-received information from the identification information that the electronic device 101 previously received and newly received identification information, the electronic device 101 may allow the newly received identification information to correspond to the non-received identification information. In another example, upon determining that the vehicle 250 is currently positioned not at an entrance or exit of a roadway, e.g., an interchange, the electronic device 101 may allow the newly received identification information of the vehicle 250 to correspond to the non-received identification information (Son et al. par. 77).
According to the cited passages and figures, examiner interpreted the information 550 in the figure 5 as the BSM (basic safety message) and examiner interpreted the identification information share the same identification element among each other like position, speed, heading and peculiarity as show in the figure 5B.
Therefore, It would have been obviously to one of ordinary skill in the art before the effective filing date of the claim invention to combine Patil et al., Luo et al., Stenneth et al. and Taguchi Seiki with Son et al. by comprising the teaching of Son et al. into the method of Patil et al., Luo et al., Stenneth et al. and Taguchi Seiki. The motivation to combine these arts is provide information categorize into variety information like identification information, position, speed, heading and peculiarity as the data type of BSMs (Basic safety message) from Son et al. reference into Patil et al., Luo et al., Stenneth et al. and Taguchi Seiki reference to help user aware of position, speed and heading of the vehicle in the proximity distance to enhance the traffic safety.
Claims 10-12, 15-16, 24-26, 31 and 34 are rejected under 35 U.S.C. 103 as being unpatentable over Patil et al. US 20180082493 in view of Luo et al. US 20190120964 and further in view of Stenneth et al. US 20220309521.
Regarding claim 10, Patil et al. teach A method for filtering detected object data, comprising: receiving a sensor data sharing message associated with a first vehicle including host data and detected object data, (Patil et al. US 20180082493 abstract; paragraph [0003]-[0006]; [0080]-[0086]; [0088]-[0095]; figures 1-9;)
As illustrated in FIG. 8, V2V systems may provide improved active safety by providing local sensor data collection abilities as well as communication options to communicate with other devices in order to share and receive additional information such as sensor information, identification information, suggested action information, hysteresis information, or other forms of information. For example, vehicle 802 may be provided with sensors and may also be equipped to obtain sensor information from other vehicles 804-812 or other devices or elements in the area such as a pedestrian using V2P and/or a traffic signal element using V2I (Patil et al. par. 82). Self-driving cars are an exciting area of innovation these days. One way to help self-driving cars is using V2V communication. FIG. 8 illustrates examples of various sensors that may be used for self-driving, including Lidar, radar, and cameras. Because these sensors are line of sight, the sensors have limitations in the amount of information the sensors may collect and then provide. V2V communication, on the other hand, is not line of sight and can work for non-line of sight cases also. This can be particularly helpful for the case where two vehicles are approaching intersections. V2V communication can be used to share sensor information between vehicles (Patil et al. par. 84).
According to the cited passages and figures, examiner interpret the vehicle 802 obtain the proximity road information around the vehicle via various of sensor. The vehicle 802 share the information through V2V communication with those vehicles in the proximity range of communication like those vehicles 804, 808, 810 and 812 as illustrated in the figure 8. Examiner interpret the pedestrian (V2P) in the proximity to the host vehicle (V2V) as one group and V2I (vehicle-to-infrastructure) like traffic light in the proximity to the host vehicle (V2V) as another group.
wherein filtering the plurality of data groups includes decoding one or more data groups of the plurality of data groups that are relevant to a risk of collision with the first vehicle, or discarding the one or more data groups of the plurality of data groups that are not relevant to the risk of collision with the first vehicle.
One advantage of such an approach is that common information may be transmitted on a given set of resources. So the interference is among vehicles that are transmitting common information, which may help control some of the interference caused due to flooding. Thus, in one case for example, a receiving vehicle on a resource may only need to decode one of the transmissions when it is known that they are transmitting common information. The vehicle may therefore have an option to select from the plurality of transmission only one to decode based on any one or more parameters and/or system information such as signal strength, noise value, error rates, proximity, location, transmitting entity, or any other determining factor or combination thereof. For example, the receiving vehicle may select to decode the signal coming from the closest vehicle. In some cases, the resource size may be set to allow for decoding information at low SNR (e.g., 0 dB or lower) (Patil et al. par. 90). It may be generally expected that vehicles that are in proximity will send basic safety messages (BSMs) to each other. Such BSMs may contain location of the vehicle along with expected trajectory. If such BSMs can also contain information from sensors being carried by the vehicles, then any given vehicle can receive such information and decide whether it is the closest to an area, among all the vehicles for which it has received the messages (Patil et al. par. 93). In some cases, only if it is the closest to an area will that vehicle transmit information on the resource associated with that area. This approach helps makes sure that there is less collision of signals from vehicles. There are several variants to this approach, however, that involve more than one vehicle transmitting. For example, instead of the closest, the two closest vehicles transmit information on the resource. As another example, only the closest and the farthest vehicle (within a certain distance) transmitting the information (to have better reuse) (Patil et al. par. 94). Yet another example approach is to have a vehicle (or vehicles) that are closest to an area from a certain direction transmit. For example, vehicles closest to an area from the north/west/east/south side among all other vehicles (for which they have received a BSM) will transmit information about that area Patil et al. par. 95).
According to the cited passages and figures, examiner interpret the system select to decode the signal coming from the closest vehicle since the object or obstacle closest to the vehicle are more relevant to the risk of collision with another vehicle in the proximity distance relative to the vehicle location.
However, Patil et al. do teach a plurality of the object in the proximate to the vehicle but Patil et al. do not explicitly teach wherein the detected object data is organized into a plurality of data groups based at least in part on a plurality of respective locations of a plurality of detected objects, the detected object data of each of the data groups is associated with respective indexing information of a first array of index values, and the host data includes the indexing information of the first array of index values; determining an operating context for a second vehicle; and filtering the plurality of data groups based at least in part on the operating context for the second vehicle and the indexing information of the first array of index values.
Luo et al. teach wherein the detected object data is organized into a plurality of data groups based at least in part on a plurality of respective locations of a plurality of detected objects, (Luo et al. US 20190120964 abstract; paragraph [0017]-[0020]; [0026]-[0037]; [0046]-[0052]; [0061]-[0063]; figures 1-7;)
One way for grouping to be performed is to determine a group of k nearest neighbors, where k is a non-zero positive integer. For example, a group of 3 nearest neighbors may be formed using received discovery information. Additional information may be used for grouping vehicles. For example, locations and directions of vehicles on roads, as well as predicted routes of vehicles on roads, may be used to divide vehicles into several groups. In some examples, groups may be formed to remain unchanged for a threshold amount of time (e.g., vehicles travelling in the same direction may be grouped, as the likelihood that a vehicle in the group will become spaced from other vehicles in the group by a threshold distance may be low). In contrast, vehicles travelling in opposite directions may not be grouped, as the vehicles may only be within the threshold distance for a very short amount of time (Luo et al. par. 20). The disclosure provides for an in-vehicle computing system of a vehicle, the in-vehicle computing system including a sensor subsystem in communication with an optical sensor, a processor, and memory storing instructions executable by the processor to instruct the optical sensor to scan an assigned region around the vehicle, receive, from the optical sensor, locally scanned data corresponding to the assigned region, process the locally scanned data to build a first portion of a three-dimensional map of an environment of the vehicle, transmit the processed scanned data to at least one other vehicle, receive additional map data from the at least one other vehicle, and build a second, different portion of the three-dimensional map using the received additional map data. In a first example of the in-vehicle computing system, the optical sensor may additionally or alternatively include a Light Detection and Ranging (LiDAR) sensor system and the locally scanned data additionally or alternatively includes point-cloud data corresponding to the assigned region. A second example of the in-vehicle computing system optionally includes the first example, and further includes the in-vehicle computing system, wherein the vehicle forms a group with the at least one other vehicle based on a proximity of the at least one other vehicle. A third example of the in-vehicle computing system optionally includes one or both of the first example and the second example, and further includes the in-vehicle computing system, wherein the instructions are further executable to receive an assignment to scan the assigned region from another vehicle of the group, each vehicle of the group being assigned a different region of a collaborative scanning area around the group, the three-dimensional map corresponding to the collaborative scanning area…………….. A seventh example of the in-vehicle computing system optionally includes one or more of the first through the sixth examples, and further includes the in-vehicle computing system, wherein the instructions are further executable to process the locally scanned data by converting the locally scanned data to a range image, obtaining a previously generated three-dimensional road map associated with the environment of the vehicle, subtracting static portions of the range image determined to be located on a surface of the three-dimensional road map, and processing only the non-subtracted portions of the range image to build the first portion of the three-dimensional map (Luo et al. par. 61).
According to the cited passages and figures, examiner interpret forms a group with at least one other vehicle based on a proximity of the at least one other vehicle and each vehicle assign to difference region for scanning the area to detect objects. Those objects is assigning as the organizing information associate the vehicle in the group to provide the plurality of detected objects information respective to the plurality of the locations.
determining an operating context for a second vehicle; and filtering the plurality of data groups based at least in part on the operating context for the second vehicle and the indexing information of the first array of index values,
FIG. 1 illustrates an example scanning environment 100 for a group of vehicles that may perform coordinated LiDAR scanning. Environment 100 includes a portion of a roadway 102, on which vehicles 104a, 104b, 104c, and 104d are travelling in a first direction, and on which vehicle 104e is travelling in a second, opposite direction. Closely driving vehicles 104b, 104c, and 104d having the same driving direction (e.g., meaning that the vehicles will not likely be far away from one another shortly/within a threshold amount of time) may be grouped temporarily. Vehicle 104a may not be grouped with vehicles 104b, 104c, and 104d because the distance between vehicle 104a and any one of the vehicles of the group may be greater than a threshold distance, or because the vehicles may be grouped according to a “three nearest neighbors” vehicle grouping parameter (in which a vehicle groups with the two nearest neighbors to the vehicle to form the three nearest neighbors group) and vehicle 104a is not one of the three nearest neighbors (e.g., each of the vehicles in the group are closer to each other vehicle in the group than to vehicle 104a). Vehicle 104e may not be grouped with vehicles 104b, 104c, and 104d because vehicle 104e is travelling in an opposite direction from the grouped vehicles (e.g., meaning that vehicle 104e will likely be far away from the grouped vehicles shortly/within a threshold amount of time). Each vehicle in the group may be communicatively connected to each other member of the group, via individual direct communication links, as illustrated in FIG. 1, and/or via another ad-hoc communication network (Luo et al. par. 17). FIG. 2 shows an example processing flow 200 for performing a coordinated scanning operation in a selected vehicle of a group of vehicles. The processing flow may begin at a discovery phase 202. Each vehicle may set associated vehicle information as being discoverable in order to ensure that the vehicles may be identified to one another when data sharing is to be performed. The identification may include LiDAR status, vehicle position, driving direction, and communication protocols that are supported by the vehicle. Accordingly, in the discovery phase, vehicles may receive and broadcast the above-described discovery information and/or send out/respond to requests for the above-described discovery information in order to identify surrounding vehicles. In some examples, a vehicle may continuously or periodically (e.g., based on a time and/or event-based trigger) broadcast vehicle information, a subset of vehicle information, or an announcement-type message indicating that vehicle information is available upon request. A given vehicle may be in a continuous discovery state (to perform actions for the discovery phase) in some examples, whereas in other examples, a vehicle may enter a discovery state responsive to a condition, such as a trigger for starting a scanning operation or a location- or navigation-based trigger (e.g., the vehicle arriving in a location likely to have multiple vehicles, the vehicle starting on a portion of a path of travel with no turn-offs for a threshold distance, etc.) (Luo et al. par. 19).
According to the cited passages and figures, examiner interpret the identification as the index information and each vehicle in the group sharing information with each other like lidar status, vehicle position and driving direction. Those information above can be treated as operating context. Also, each host vehicle can treat other vehicles in the group as the second or third vehicle. For example, in the figure 1, vehicle 104d can treat vehicle 104b as the second vehicle and 104c as the third vehicle or vehicle 104c can treat vehicle 104d as the second vehicle and 104b as the third vehicle. They are all sharing their operation information with each other base on the proximity group status.
Therefore, It would have been obviously to one of ordinary skill in the art before the effective filing date of the claim invention to combine Patil et al. and Luo et al. by comprising the teaching of Luo et al. into the method of Patil et al.. The motivation to combine these arts is to assign the group for the vehicles in the proximity distance to each other and each of vehicle in the group can assign a difference region to detect the object or obstacle of the environment surrounding the vehicle from Luo et al. reference into Patil et al. reference to cover the large environment to enhance the traffic safety.
However, the combination of Patil et al. and Luo et al. do teach the index information but the combination of Patil et al. and Luo et al. do not explicitly teach the detected object data of each of the data groups is associated with respective indexing information of a first array of index values, and the host data includes the indexing information of the first array of index values.
Stenneth et al. teach the detected object data of each of the data groups is associated with respective indexing information of a first array of index values, and the host data includes the indexing information of the first array of index values; (Stenneth et al. US 20220309521 abstract; paragraphs [0005]-[0007]; [0017]-[0020]; [0022]-[0026]; [0028]-[0031]; [0033]; [0049]; [0069]; [0122]; [0125]-[0128]; figures 1-8;)
The distance data detection sensor may include a laser range finder that rotates a mirror directing a laser to the surroundings or vicinity of the collection vehicle on a roadway or another collection device on any type of pathway. A connected vehicle includes a communication device and an environment sensor array for detecting and reporting the surroundings of the shared vehicle 124 to the mapping system 121. The connected vehicle may include an integrated communication device coupled with an in-dash navigation system. The connected vehicle may include an ad-hoc communication device such as a mobile device or smartphone in communication with a vehicle system. The communication device connects the vehicle to a network 127 including at least the mapping system 121. The network 127 may be the Internet or connected to the internet (Stenneth et al. par. 25). Vision-based techniques are used by the device 122 to acquire information about candidates 135 in the area around the shared vehicle 124. Video data, image data, or other sensor data may be collected and processed to identify features or attributes of a candidate 135. Image recognition methods or classifiers such as neural networks may be used. The collected information may be processed and transformed into Boolean values or scores that are input into the computation for the interest index value for a candidate 135 at a particular time. The shared vehicle 124 identifies a search radius that is considered for collecting parameters to be used for the interest index computation. The search radius may be dynamic and may depend on different features or variables of the location such as pedestrian density, street type, vehicle orientation, weather, etc. When the search function (e.g., looking for a passenger) is active, the device 122 monitors candidates 135 that are inside the search radius. The device 122 may collect information about the candidate 135 using respective sensors such as cameras, radar, LIDAR, accelerometers, gyroscopes, GPS, ultrasonic sensors, etc. The collected information may be used to determine Boolean values or scores for parameters such as the candidate's context (alone, in group, carrying something, etc.), a candidate's walking maneuvers and the possible detour made to go closer to that vehicle (accelerometer, gyroscope, GPS), the proximity of the vehicle (GPS, radar, LIDAR, Ultrasonic sensors), eye contact with the vehicle (based on cameras), facial expression detection (based on cameras), a possible reaction when the vehicle communicates with the candidate 135 (based on cameras), the presence of other “bookable” vehicles in the direct vicinity (i.e. “competitor vehicles), and environmental attributes (Weather, etc.) among others. Information may be collected by the device 122 constantly or at set intervals (Stenneth et al. par. 28).
According to the cited passages and figures, examiner interpret the feature or attribute of pedestrian 135 as an interest index value in the proximity with the vehicle which had been detect by an environment sensor array to monitor the objects surround the vehicle.
Therefore, It would have been obviously to one of ordinary skill in the art before the effective filing date of the claim invention to combine Patil et al. and Luo et al. with Stenneth et al. by comprising the teaching of Stenneth et al. into the method of Patil et al. and Luo et al.. The motivation to combine these arts is to provide a simple substitution of index value for pedestrian from Stenneth et al. reference into Patil et al. and Luo et al. so the system can utilize the index value for determine the object in the proximity to the vehicle within the radius parameter to avoid collision.
Regarding claim 11, the combination of Patil et al., Luo et al. and Stenneth et al. disclose The method of claim 10 wherein the plurality of data groups define an area around the first vehicle.
Once grouped, the combined scanning processes of the vehicles may cover a viewing range 106 representing a 360 degree area around the group of vehicles that may be processed for display as a three-dimensional map of the environment of the vehicles. In the illustrated example, the scanning and processing tasks are distributed such that vehicle 104b is responsible for scanning and processing LiDAR data in area 106a, vehicle 104c is responsible for scanning and processing LiDAR data in area 106b, and vehicle 104d is responsible for scanning and processing LiDAR data in area 106c. When combined, areas 106a-c cover the full viewing range 106 (Luo et al. par. 18). One way for grouping to be performed is to determine a group of k nearest neighbors, where k is a non-zero positive integer. For example, a group of 3 nearest neighbors may be formed using received discovery information. Additional information may be used for grouping vehicles. For example, locations and directions of vehicles on roads, as well as predicted routes of vehicles on roads, may be used to divide vehicles into several groups. In some examples, groups may be formed to remain unchanged for a threshold amount of time (e.g., vehicles travelling in the same direction may be grouped, as the likelihood that a vehicle in the group will become spaced from other vehicles in the group by a threshold distance may be low). In contrast, vehicles travelling in opposite directions may not be grouped, as the vehicles may only be within the threshold distance for a very short amount of time (Luo et al. par. 20).
Regarding claim 12, the combination of Patil et al., Luo et al. and Stenneth et al. disclose The method of claim 11 wherein the area is a circle around the first vehicle, and the host data includes a first information element to define a radius of the circle.
Each vehicle uses a predefined search radius for a location that is considered for collecting data to be used for the interest index computation. The predefined search radius may be defined based on the capabilities of the shared vehicle 124 (sensor quality/range) and/or the context of the location. The data may be constantly collected. Alternatively, some data may be collected at certain intervals, e.g., periodically every 1, 2, 5, 10 seconds, etc. The shared vehicle 124 may be equipped and/or may communicate with one or more sensors configured to acquire information about a candidate 135. For example, the shared vehicle 124 may be equipped with cameras, a Radar system, and/or a LiDAR system. The shared vehicle 124 may use cameras to detect attributes of the candidate 135, walking maneuvers and trajectory, proximity of the candidate 135, eye contact or facial expression of the candidate 135, or a reaction after an attempt by the shared vehicle 124 to contact the candidate 135 (Stenneth et al. par. 49).
Regarding claim 15, the combination of Patil et al., Luo et al. and Stenneth et al. disclose The method of claim 10 wherein the plurality of data groups are associated with a heading of each of the plurality of detected objects.
It may be generally expected that vehicles that are in proximity will send basic safety messages (BSMs) to each other. Such BSMs may contain location of the vehicle along with expected trajectory. If such BSMs can also contain information from sensors being carried by the vehicles, then any given vehicle can receive such information and decide whether it is the closest to an area, among all the vehicles for which it has received the messages. Further, according to one or more examples, the one or more metrics may be derived based on basic safety messages (BSMs) transmitted by the one or more of the vehicles. In some cases, the one or more vehicles may transmit their sensor capability along with BSMs (Patil et al. par. 93).
According to the cited passages and figures examiner interpret vehicle location along with expected trajectory of the vehicle is same as the heading of each of the one or more detect objects which similar to the figure 8 of Patil et. al. reference.
Regarding claim 16, the combination of Patil et al., Luo et al. and Stenneth et al. disclose The method of claim 10 further comprising receiving a basic safety message including a current location of the first vehicle, wherein the sensor data sharing message is associated with the basic safety message.
It may be generally expected that vehicles that are in proximity will send basic safety messages (BSMs) to each other. Such BSMs may contain location of the vehicle along with expected trajectory. If such BSMs can also contain information from sensors being carried by the vehicles, then any given vehicle can receive such information and decide whether it is the closest to an area, among all the vehicles for which it has received the messages. Further, according to one or more examples, the one or more metrics may be derived based on basic safety messages (BSMs) transmitted by the one or more of the vehicles. In some cases, the one or more vehicles may transmit their sensor capability along with BSMs (Patil et al. par. 93).
Regarding claim 24, Patil et al. teach An apparatus, comprising: a memory; at least one transceiver; at least one processor communicatively coupled to the memory and the at least one transceiver, and configured to: receive a sensor data sharing message associated with a first vehicle including host data and detected object data, (Patil et al. US 20180082493 abstract; paragraph [0003]-[0006]; [0008]-[0010]; [0066]-[0070] [0080]-[0086]; [0088]-[0095]; [0106]-[0108]; figures 1-9;)
Certain aspects of the present disclosure provide a method that may be performed, for example, by a communication device integrated in or installed on a vehicle. The method generally includes determining, based a given location, a set of frequency resources to be used for transmitting sensor information obtained at one or more vehicles for that given location, and utilizing the set of frequency resources to transmit sensor information or monitor for sensor information (Patil et al. par. 9). For example, the wireless device 502 may be configured to perform operations 900 illustrated in FIG. 9 as well as other operations described herein (Patil et al. par. 66). The wireless device 502 may include a processor 504 that controls operation of the wireless device 502. The processor 504 may also be referred to as a central processing unit (CPU). Memory 506, which may include both read-only memory (ROM) and random access memory (RAM), provides instructions and data to the processor 504 (Patil et al. par. 67). The wireless device 502 may also include a housing 508 that may include a transmitter 510 and a receiver 512 to allow transmission and reception of data between the wireless device 502 and a remote location. The transmitter 510 and receiver 512 may be combined into a transceiver 514 (Patil et al. par. 68) . As illustrated in FIG. 8, V2V systems may provide improved active safety by providing local sensor data collection abilities as well as communication options to communicate with other devices in order to share and receive additional information such as sensor information, identification information, suggested action information, hysteresis information, or other forms of information. For example, vehicle 802 may be provided with sensors and may also be equipped to obtain sensor information from other vehicles 804-812 or other devices or elements in the area such as a pedestrian using V2P and/or a traffic signal element using V2I (Patil et al. par. 82). Self-driving cars are an exciting area of innovation these days. One way to help self-driving cars is using V2V communication. FIG. 8 illustrates examples of various sensors that may be used for self-driving, including Lidar, radar, and cameras. Because these sensors are line of sight, the sensors have limitations in the amount of information the sensors may collect and then provide. V2V communication, on the other hand, is not line of sight and can work for non-line of sight cases also. This can be particularly helpful for the case where two vehicles are approaching intersections. V2V communication can be used to share sensor information between vehicles (Patil et al. par. 84).
According to the cited passages and figures, examiner interpret the vehicle 802 obtain the proximity road information around the vehicle via various of sensor. The vehicle 802 share the information through V2V communication with those vehicles in the proximity range of communication like those vehicles 804, 808, 810 and 812 as illustrated in the figure 8.
and decode the plurality of data groups based at least in part on the operating context for the second vehicle and the indexing information associated with the plurality of data groups, wherein the at least one processor configured to decode the plurality of data groups is further configured to: decode one or more data groups of the plurality of data groups that are relevant to a risk of collision with the first vehicle, or discard one or more data groups of the plurality of data groups that are not relevant to the risk of collision with the first vehicle.
One advantage of such an approach is that common information may be transmitted on a given set of resources. So the interference is among vehicles that are transmitting common information, which may help control some of the interference caused due to flooding. Thus, in one case for example, a receiving vehicle on a resource may only need to decode one of the transmissions when it is known that they are transmitting common information. The vehicle may therefore have an option to select from the plurality of transmission only one to decode based on any one or more parameters and/or system information such as signal strength, noise value, error rates, proximity, location, transmitting entity, or any other determining factor or combination thereof. For example, the receiving vehicle may select to decode the signal coming from the closest vehicle. In some cases, the resource size may be set to allow for decoding information at low SNR (e.g., 0 dB or lower) (Patil et al. par. 90). It may be generally expected that vehicles that are in proximity will send basic safety messages (BSMs) to each other. Such BSMs may contain location of the vehicle along with expected trajectory. If such BSMs can also contain information from sensors being carried by the vehicles, then any given vehicle can receive such information and decide whether it is the closest to an area, among all the vehicles for which it has received the messages (Patil et al. par. 93). In some cases, only if it is the closest to an area will that vehicle transmit information on the resource associated with that area. This approach helps makes sure that there is less collision of signals from vehicles. There are several variants to this approach, however, that involve more than one vehicle transmitting. For example, instead of the closest, the two closest vehicles transmit information on the resource. As another example, only the closest and the farthest vehicle (within a certain distance) transmitting the information (to have better reuse) (Patil et al. par. 94). Yet another example approach is to have a vehicle (or vehicles) that are closest to an area from a certain direction transmit. For example, vehicles closest to an area from the north/west/east/south side among all other vehicles (for which they have received a BSM) will transmit information about that area Patil et al. par. 95).
According to the cited passages and figures, examiner interpret the system select to decode the signal coming from the closest vehicle since the object or obstacle closest to the vehicle are more relevant to the risk of collision with another vehicle in the proximity distance relative to the vehicle location.
However, Patil et al. do teach a plurality of the object in the proximate to the vehicle but Patil et al. do not explicitly teach wherein the detected object data is organized into a plurality of data groups based at least in part on a plurality of respective locations of a plurality of detected objects, the detected object data of each of the data groups is associated with respective indexing information of an array of index values, and the host data includes the indexing information of the array of index values; determine an operating context for a second vehicle.
Luo et al. teach wherein the detected object data is organized into a plurality of data groups based at least in part on a plurality of respective locations of a plurality of detected objects, (Luo et al. US 20190120964 abstract; paragraph [0017]-[0020]; [0026]-[0037]; [0061]-[0063]; figures 1-7;)
One way for grouping to be performed is to determine a group of k nearest neighbors, where k is a non-zero positive integer. For example, a group of 3 nearest neighbors may be formed using received discovery information. Additional information may be used for grouping vehicles. For example, locations and directions of vehicles on roads, as well as predicted routes of vehicles on roads, may be used to divide vehicles into several groups. In some examples, groups may be formed to remain unchanged for a threshold amount of time (e.g., vehicles travelling in the same direction may be grouped, as the likelihood that a vehicle in the group will become spaced from other vehicles in the group by a threshold distance may be low). In contrast, vehicles travelling in opposite directions may not be grouped, as the vehicles may only be within the threshold distance for a very short amount of time (Luo et al. par. 20). The disclosure provides for an in-vehicle computing system of a vehicle, the in-vehicle computing system including a sensor subsystem in communication with an optical sensor, a processor, and memory storing instructions executable by the processor to instruct the optical sensor to scan an assigned region around the vehicle, receive, from the optical sensor, locally scanned data corresponding to the assigned region, process the locally scanned data to build a first portion of a three-dimensional map of an environment of the vehicle, transmit the processed scanned data to at least one other vehicle, receive additional map data from the at least one other vehicle, and build a second, different portion of the three-dimensional map using the received additional map data. In a first example of the in-vehicle computing system, the optical sensor may additionally or alternatively include a Light Detection and Ranging (LiDAR) sensor system and the locally scanned data additionally or alternatively includes point-cloud data corresponding to the assigned region. A second example of the in-vehicle computing system optionally includes the first example, and further includes the in-vehicle computing system, wherein the vehicle forms a group with the at least one other vehicle based on a proximity of the at least one other vehicle. A third example of the in-vehicle computing system optionally includes one or both of the first example and the second example, and further includes the in-vehicle computing system, wherein the instructions are further executable to receive an assignment to scan the assigned region from another vehicle of the group, each vehicle of the group being assigned a different region of a collaborative scanning area around the group, the three-dimensional map corresponding to the collaborative scanning area…………….. A seventh example of the in-vehicle computing system optionally includes one or more of the first through the sixth examples, and further includes the in-vehicle computing system, wherein the instructions are further executable to process the locally scanned data by converting the locally scanned data to a range image, obtaining a previously generated three-dimensional road map associated with the environment of the vehicle, subtracting static portions of the range image determined to be located on a surface of the three-dimensional road map, and processing only the non-subtracted portions of the range image to build the first portion of the three-dimensional map (Luo et al. par. 61).
According to the cited passages and figures, examiner interpret forms a group with at least one other vehicle based on a proximity of the at least one other vehicle and each vehicle assign to difference region for scanning the area to detect objects. Those objects is assigning as the organizing information associate the vehicle in the group to provide the plurality of detected objects information respective to the plurality of the locations.
and the host data includes the indexing information of the array of index values; determine an operating context for a second vehicle;
FIG. 1 illustrates an example scanning environment 100 for a group of vehicles that may perform coordinated LiDAR scanning. Environment 100 includes a portion of a roadway 102, on which vehicles 104a, 104b, 104c, and 104d are travelling in a first direction, and on which vehicle 104e is travelling in a second, opposite direction. Closely driving vehicles 104b, 104c, and 104d having the same driving direction (e.g., meaning that the vehicles will not likely be far away from one another shortly/within a threshold amount of time) may be grouped temporarily. Vehicle 104a may not be grouped with vehicles 104b, 104c, and 104d because the distance between vehicle 104a and any one of the vehicles of the group may be greater than a threshold distance, or because the vehicles may be grouped according to a “three nearest neighbors” vehicle grouping parameter (in which a vehicle groups with the two nearest neighbors to the vehicle to form the three nearest neighbors group) and vehicle 104a is not one of the three nearest neighbors (e.g., each of the vehicles in the group are closer to each other vehicle in the group than to vehicle 104a). Vehicle 104e may not be grouped with vehicles 104b, 104c, and 104d because vehicle 104e is travelling in an opposite direction from the grouped vehicles (e.g., meaning that vehicle 104e will likely be far away from the grouped vehicles shortly/within a threshold amount of time). Each vehicle in the group may be communicatively connected to each other member of the group, via individual direct communication links, as illustrated in FIG. 1, and/or via another ad-hoc communication network (Luo et al. par. 17). FIG. 2 shows an example processing flow 200 for performing a coordinated scanning operation in a selected vehicle of a group of vehicles. The processing flow may begin at a discovery phase 202. Each vehicle may set associated vehicle information as being discoverable in order to ensure that the vehicles may be identified to one another when data sharing is to be performed. The identification may include LiDAR status, vehicle position, driving direction, and communication protocols that are supported by the vehicle. Accordingly, in the discovery phase, vehicles may receive and broadcast the above-described discovery information and/or send out/respond to requests for the above-described discovery information in order to identify surrounding vehicles. In some examples, a vehicle may continuously or periodically (e.g., based on a time and/or event-based trigger) broadcast vehicle information, a subset of vehicle information, or an announcement-type message indicating that vehicle information is available upon request. A given vehicle may be in a continuous discovery state (to perform actions for the discovery phase) in some examples, whereas in other examples, a vehicle may enter a discovery state responsive to a condition, such as a trigger for starting a scanning operation or a location- or navigation-based trigger (e.g., the vehicle arriving in a location likely to have multiple vehicles, the vehicle starting on a portion of a path of travel with no turn-offs for a threshold distance, etc.) (Luo et al. par. 19). One way for grouping to be performed is to determine a group of k nearest neighbors, where k is a non-zero positive integer. For example, a group of 3 nearest neighbors may be formed using received discovery information. Additional information may be used for grouping vehicles. For example, locations and directions of vehicles on roads, as well as predicted routes of vehicles on roads, may be used to divide vehicles into several groups. In some examples, groups may be formed to remain unchanged for a threshold amount of time (e.g., vehicles travelling in the same direction may be grouped, as the likelihood that a vehicle in the group will become spaced from other vehicles in the group by a threshold distance may be low). In contrast, vehicles travelling in opposite directions may not be grouped, as the vehicles may only be within the threshold distance for a very short amount of time (Luo et al. par. 20).
According to the cited passages and figures, examiner interpret the identification as the index information and each vehicle in the group sharing information with each other like lidar status, vehicle position and driving direction. Those information above can be treated as operating context. Also, each host vehicle can treat other vehicles in the group as the second or third vehicle. For example, in the figure 1, vehicle 104d can treat vehicle 104b as the second vehicle and 104c as the third vehicle or vehicle 104c can treat vehicle 104d as the second vehicle and 104b as the third vehicle. They are all sharing their operation information with each other base on the proximity group status.
Therefore, It would have been obviously to one of ordinary skill in the art before the effective filing date of the claim invention to combine Patil et al. and Luo et al. by comprising the teaching of Luo et al. into the system of Patil et al.. The motivation to combine these arts is to assign the group for the vehicles in the proximity distance to each other and each of vehicle in the group can assign a difference region to detect the object or obstacle of the environment surrounding the vehicle from Luo et al. reference into Patil et al. reference to cover the large environment to enhance the traffic safety.
However, the combination of Patil et al. and Luo et al. do teach the index information but the combination of Patil et al. and Luo et al. do not explicitly teach the detected object data of each of the data groups is associated with respective indexing information of an array of index values.
Stenneth et al. teach the detected object data of each of the data groups is associated with respective indexing information of an array of index values, (Stenneth et al. US 20220309521 abstract; paragraphs [0005]-[0007]; [0017]-[0020]; [0022]-[0026]; [0028]-[0031]; [0033]; [0049]; [0069]; [0122]; [0125]-[0128]; figures 1-8;)
The distance data detection sensor may include a laser range finder that rotates a mirror directing a laser to the surroundings or vicinity of the collection vehicle on a roadway or another collection device on any type of pathway. A connected vehicle includes a communication device and an environment sensor array for detecting and reporting the surroundings of the shared vehicle 124 to the mapping system 121. The connected vehicle may include an integrated communication device coupled with an in-dash navigation system. The connected vehicle may include an ad-hoc communication device such as a mobile device or smartphone in communication with a vehicle system. The communication device connects the vehicle to a network 127 including at least the mapping system 121. The network 127 may be the Internet or connected to the internet (Stenneth et al. par. 25). Vision-based techniques are used by the device 122 to acquire information about candidates 135 in the area around the shared vehicle 124. Video data, image data, or other sensor data may be collected and processed to identify features or attributes of a candidate 135. Image recognition methods or classifiers such as neural networks may be used. The collected information may be processed and transformed into Boolean values or scores that are input into the computation for the interest index value for a candidate 135 at a particular time. The shared vehicle 124 identifies a search radius that is considered for collecting parameters to be used for the interest index computation. The search radius may be dynamic and may depend on different features or variables of the location such as pedestrian density, street type, vehicle orientation, weather, etc. When the search function (e.g., looking for a passenger) is active, the device 122 monitors candidates 135 that are inside the search radius. The device 122 may collect information about the candidate 135 using respective sensors such as cameras, radar, LIDAR, accelerometers, gyroscopes, GPS, ultrasonic sensors, etc. The collected information may be used to determine Boolean values or scores for parameters such as the candidate's context (alone, in group, carrying something, etc.), a candidate's walking maneuvers and the possible detour made to go closer to that vehicle (accelerometer, gyroscope, GPS), the proximity of the vehicle (GPS, radar, LIDAR, Ultrasonic sensors), eye contact with the vehicle (based on cameras), facial expression detection (based on cameras), a possible reaction when the vehicle communicates with the candidate 135 (based on cameras), the presence of other “bookable” vehicles in the direct vicinity (i.e. “competitor vehicles), and environmental attributes (Weather, etc.) among others. Information may be collected by the device 122 constantly or at set intervals (Stenneth et al. par. 28).
According to the cited passages and figures, examiner interpret the feature or attribute of pedestrian 135 as an interest index value in the proximity with the vehicle which had been detect by an environment sensor array to monitor the objects surround the vehicle.
Therefore, It would have been obviously to one of ordinary skill in the art before the effective filing date of the claim invention to combine Patil et al. and Luo et al. with Stenneth et al. by comprising the teaching of Stenneth et al. into the system of Patil et al. and Luo et al.. The motivation to combine these arts is to provide a simple substitution of index value for pedestrian from Stenneth et al. reference into Patil et al. and Luo et al. so the system can utilize the index value for determine the object in the proximity to the vehicle within the radius parameter to avoid collision.
Regarding claim 25, the combination of Patil et al., Luo et al. and Stenneth et al. disclose The apparatus of claim 24 wherein the plurality of data groups are associated with a heading of each of the plurality of detected objects.
It may be generally expected that vehicles that are in proximity will send basic safety messages (BSMs) to each other. Such BSMs may contain location of the vehicle along with expected trajectory. If such BSMs can also contain information from sensors being carried by the vehicles, then any given vehicle can receive such information and decide whether it is the closest to an area, among all the vehicles for which it has received the messages. Further, according to one or more examples, the one or more metrics may be derived based on basic safety messages (BSMs) transmitted by the one or more of the vehicles. In some cases, the one or more vehicles may transmit their sensor capability along with BSMs (Patil et al. par. 93).
According to the cited passages and figures examiner interpret vehicle location along with expected trajectory of the vehicle is same as the heading of each of the one or more detect objects which similar to the figure 8 of Patil et. al. reference.
Regarding claim 26, the combination of Patil et al., Luo et al. and Stenneth et al. disclose The apparatus of claim 24 wherein the at least one processor is further configured to receive a basic safety message including a current location of the first vehicle, wherein the sensor data sharing message is associated with the basic safety message.
It may be generally expected that vehicles that are in proximity will send basic safety messages (BSMs) to each other. Such BSMs may contain location of the vehicle along with expected trajectory. If such BSMs can also contain information from sensors being carried by the vehicles, then any given vehicle can receive such information and decide whether it is the closest to an area, among all the vehicles for which it has received the messages. Further, according to one or more examples, the one or more metrics may be derived based on basic safety messages (BSMs) transmitted by the one or more of the vehicles. In some cases, the one or more vehicles may transmit their sensor capability along with BSMs (Patil et al. par. 93).
Regarding claim 31, the combination of Patil et al., Luo et al. and Stenneth et al. disclose The apparatus of claim 24 wherein the array of index values identifies data locations of the data groups in the detected object data.
The distance data detection sensor may include a laser range finder that rotates a mirror directing a laser to the surroundings or vicinity of the collection vehicle on a roadway or another collection device on any type of pathway. A connected vehicle includes a communication device and an environment sensor array for detecting and reporting the surroundings of the shared vehicle 124 to the mapping system 121. The connected vehicle may include an integrated communication device coupled with an in-dash navigation system. The connected vehicle may include an ad-hoc communication device such as a mobile device or smartphone in communication with a vehicle system. The communication device connects the vehicle to a network 127 including at least the mapping system 121. The network 127 may be the Internet or connected to the internet (Stenneth et al. par. 25). Vision-based techniques are used by the device 122 to acquire information about candidates 135 in the area around the shared vehicle 124. Video data, image data, or other sensor data may be collected and processed to identify features or attributes of a candidate 135. Image recognition methods or classifiers such as neural networks may be used. The collected information may be processed and transformed into Boolean values or scores that are input into the computation for the interest index value for a candidate 135 at a particular time. The shared vehicle 124 identifies a search radius that is considered for collecting parameters to be used for the interest index computation. The search radius may be dynamic and may depend on different features or variables of the location such as pedestrian density, street type, vehicle orientation, weather, etc. When the search function (e.g., looking for a passenger) is active, the device 122 monitors candidates 135 that are inside the search radius. The device 122 may collect information about the candidate 135 using respective sensors such as cameras, radar, LIDAR, accelerometers, gyroscopes, GPS, ultrasonic sensors, etc. The collected information may be used to determine Boolean values or scores for parameters such as the candidate's context (alone, in group, carrying something, etc.), a candidate's walking maneuvers and the possible detour made to go closer to that vehicle (accelerometer, gyroscope, GPS), the proximity of the vehicle (GPS, radar, LIDAR, Ultrasonic sensors), eye contact with the vehicle (based on cameras), facial expression detection (based on cameras), a possible reaction when the vehicle communicates with the candidate 135 (based on cameras), the presence of other “bookable” vehicles in the direct vicinity (i.e. “competitor vehicles), and environmental attributes (Weather, etc.) among others. Information may be collected by the device 122 constantly or at set intervals (Stenneth et al. par. 28).
According to the cited passages and figures, examiner interpreted the feature or attribute of pedestrian 135 as an interest index value in the proximity with the vehicle which had been detect by an environment sensor array to monitor the objects surround the vehicle.
Regarding claim 34, the combination of Patil et al., Luo et al. and Stenneth et al. disclose The apparatus of claim 24, wherein to determine the operating context the at least one processor is configured to consider positions and trajectories of the first vehicle and the second vehicle, and a geometry of an environment of the first vehicle.
According to certain aspects, such a mapping may also be used to decide which vehicle should transmit on the resource. The decision may be based on various metrics (such as distance/proximity or direction). For example, deciding whether or not to transmit sensor information about a set of one or more locations on the set of frequency resources associated with those locations may be based on one or more metrics. The one or more metrics may include, for example, a location of one or more of the one or more vehicles capable of transmitting the sensor information (par. 92). It may be generally expected that vehicles that are in proximity will send basic safety messages (BSMs) to each other. Such BSMs may contain location of the vehicle along with expected trajectory. If such BSMs can also contain information from sensors being carried by the vehicles, then any given vehicle can receive such information and decide whether it is the closest to an area, among all the vehicles for which it has received the messages. Further, according to one or more examples, the one or more metrics may be derived based on basic safety messages (BSMs) transmitted by the one or more of the vehicles. In some cases, the one or more vehicles may transmit their sensor capability along with BSMs (Patil et al. par. 93).
As show in the figure 8, examiner interpret vehicle 802 as the first vehicle equip with variety of sensors and include the V2V communication system that cable of sharing information like location and trajectory of other proximity vehicle like 804-812 show in the figure 8. Examiner also interpret at least one proximity vehicle relative to vehicle 802 as a second vehicle.
Claims 17 and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Patil et al. US 20180082493, in view of Luo et al. US 20190120964, in view of Stenneth et al. US 20220309521 and further in view of Son et al. US 20190035277.
Regarding claim 17, the combination of Patil et al., Luo et al. and Stenneth et al. teach all the limitation in the claim 16.
The combination of Patil et al., Luo et al. and Stenneth et al. do not explicitly teach The method of claim 16 wherein the basic safety message and the sensor data sharing message each include an identification information element containing the same identification value.
Son et al. teach The method of claim 16 wherein the basic safety message and the sensor data sharing message each include an identification information element containing the same identification value. (Son et al. US 20190035277 abstract; paragraph [0006]-[0007]; [0056]-[0060]; [0077]; figures 1-16;)
The electronic device 101 (e.g., the processor 120) included in the vehicle 220 may manage, information (e.g., driving log) such as those shown in FIG. 5B. For example, the information 550 may be categorized into identification information 551, position 552, speed 553, heading 554, or peculiarity 555 and may include data types for at least part of BSMs or PSMs. The electronic device 101 may manage various message set types. As described above, the vehicle 220 may receive a communication signal 511, a communication signal 521, or a communication signal 531 at the first time t1. The communication signal 511 may include identification information V1 about the vehicle 240, the communication signal 521 may include identification information V2 about the vehicle 250, and the communication signal 531 may include identification information P1 about the electronic device 101. For example, the electronic device 101 may also determine that the identification information V2 corresponds to the identification information V3 based on the fact that the speed S2 and heading θ2 corresponding to the identification information V2 are the same as the speed S2 and heading θ2, respectively, corresponding to the identification information V3. In other words, the electronic device 101 may determine the correspondence between the pieces of identification information by using other information than the position of the vehicle. Upon identifying the non-received information from the identification information that the electronic device 101 previously received and newly received identification information, the electronic device 101 may allow the newly received identification information to correspond to the non-received identification information. In another example, upon determining that the vehicle 250 is currently positioned not at an entrance or exit of a roadway, e.g., an interchange, the electronic device 101 may allow the newly received identification information of the vehicle 250 to correspond to the non-received identification information (Son et al. par. 77).
According to the cited passages and figures, examiner interpreted the information 550 in the figure 5 as the BSM (basic safety message) and examiner interpreted the identification information share the same identification element among each other like position, speed, heading and peculiarity as show in the figure 5B.
Therefore, It would have been obviously to one of ordinary skill in the art before the effective filing date of the claim invention to combine Patil et al., Luo et al. and Stenneth et al. with Son et al. by comprising the teaching of Son et al. into the method of Patil et al., Luo et al. and Stenneth et al.. The motivation to combine these arts is provide information categorize into variety information like identification information, position, speed, heading and peculiarity as the data type of BSMs (Basic safety message) from Son et al. reference into Patil et al., Luo et al. and Stenneth et al. reference to help user aware of position, speed and heading of the vehicle in the proximity distance to enhance the traffic safety.
Regarding claim 27, the combination of Patil et al., Luo et al., Stenneth et al. and Son et al. disclose The apparatus of claim 26 wherein the basic safety message and the sensor data sharing message each include an identification information element containing the same identification value.
The electronic device 101 (e.g., the processor 120) included in the vehicle 220 may manage, information (e.g., driving log) such as those shown in FIG. 5B. For example, the information 550 may be categorized into identification information 551, position 552, speed 553, heading 554, or peculiarity 555 and may include data types for at least part of BSMs or PSMs. The electronic device 101 may manage various message set types. As described above, the vehicle 220 may receive a communication signal 511, a communication signal 521, or a communication signal 531 at the first time t1. The communication signal 511 may include identification information V1 about the vehicle 240, the communication signal 521 may include identification information V2 about the vehicle 250, and the communication signal 531 may include identification information P1 about the electronic device 101. For example, the electronic device 101 may also determine that the identification information V2 corresponds to the identification information V3 based on the fact that the speed S2 and heading θ2 corresponding to the identification information V2 are the same as the speed S2 and heading θ2, respectively, corresponding to the identification information V3. In other words, the electronic device 101 may determine the correspondence between the pieces of identification information by using other information than the position of the vehicle. Upon identifying the non-received information from the identification information that the electronic device 101 previously received and newly received identification information, the electronic device 101 may allow the newly received identification information to correspond to the non-received identification information. In another example, upon determining that the vehicle 250 is currently positioned not at an entrance or exit of a roadway, e.g., an interchange, the electronic device 101 may allow the newly received identification information of the vehicle 250 to correspond to the non-received identification information (Son et al. par. 77).
According to the cited passages and figures, examiner interpreted the information 550 in the figure 5 as the BSM (basic safety message) and examiner interpreted the identification information share the same identification element among each other like position, speed, heading and peculiarity as show in the figure 5B.
Claims 18-19, 29, 37 and 39 are rejected under 35 U.S.C. 103 as being unpatentable over Patil et al. US 20180082493, in view of Luo et al. US 20190120964, in view of Stenneth et al. US 20220309521 and further in view of Lee US 20220219690.
Regarding claim 37, the combination of Patil et al., Luo et al. and Stenneth et al. teach wherein the operating context comprises a relevancy distance associated with the second vehicle, wherein the filtering of the plurality of data groups comprises: and decoding the one or more data groups of the plurality of data groups corresponding to the one or more sub-areas or discarding the one or more data groups of the plurality of data groups not corresponding to the one or more sub-areas.
One advantage of such an approach is that common information may be transmitted on a given set of resources. So the interference is among vehicles that are transmitting common information, which may help control some of the interference caused due to flooding. Thus, in one case for example, a receiving vehicle on a resource may only need to decode one of the transmissions when it is known that they are transmitting common information. The vehicle may therefore have an option to select from the plurality of transmission only one to decode based on any one or more parameters and/or system information such as signal strength, noise value, error rates, proximity, location, transmitting entity, or any other determining factor or combination thereof. For example, the receiving vehicle may select to decode the signal coming from the closest vehicle. In some cases, the resource size may be set to allow for decoding information at low SNR (e.g., 0 dB or lower) (Patil et al. par. 90). It may be generally expected that vehicles that are in proximity will send basic safety messages (BSMs) to each other. Such BSMs may contain location of the vehicle along with expected trajectory. If such BSMs can also contain information from sensors being carried by the vehicles, then any given vehicle can receive such information and decide whether it is the closest to an area, among all the vehicles for which it has received the messages (Patil et al. par. 93). In some cases, only if it is the closest to an area will that vehicle transmit information on the resource associated with that area. This approach helps makes sure that there is less collision of signals from vehicles. There are several variants to this approach, however, that involve more than one vehicle transmitting. For example, instead of the closest, the two closest vehicles transmit information on the resource. As another example, only the closest and the farthest vehicle (within a certain distance) transmitting the information (to have better reuse) (Patil et al. par. 94). Yet another example approach is to have a vehicle (or vehicles) that are closest to an area from a certain direction transmit. For example, vehicles closest to an area from the north/west/east/south side among all other vehicles (for which they have received a BSM) will transmit information about that area Patil et al. par. 95).
According to the cited passages and figures, examiner interpret the system select to decode the signal coming from the closest vehicle since the object or obstacle closest to the vehicle are more relevant to the risk of collision with another vehicle in the proximity distance relative to the vehicle location.
The combination of Patil et al., Luo et al. and Stenneth et al. do not explicitly teach The method of claim 10, wherein the host data comprises an area of interest proximate to the first vehicle and a plurality of sub-areas of the area of interest, wherein the detected object data is organized into the plurality of data groups corresponding to the plurality of sub-areas, determining one or more sub-areas of the plurality of sub-areas with the risk of collision with the first vehicle based on the relevancy distance intersecting the one or more sub-areas.
Lee teaches The method of claim 10, wherein the host data comprises an area of interest proximate to the first vehicle and a plurality of sub-areas of the area of interest, wherein the detected object data is organized into the plurality of data groups corresponding to the plurality of sub-areas, determining one or more sub-areas of the plurality of sub-areas with the risk of collision with the first vehicle based on the relevancy distance intersecting the one or more sub-areas; (Lee US 20220219690 paragraphs [0017]-[0019]; [0058]-[0067]; figures 1-5;)
The computing device may classify a detection zone in which a neighboring vehicle is detected into a plurality of sub-zones according to the level of collision risk based on the location information of the neighboring vehicle and compute the target speed based on the location information of the neighboring vehicle detected in the detection zone (Lee par. 17). As shown in FIG. 4, the computing device 30 may classify the detection zone of the neighboring vehicle 200 into a plurality of sub-zones according to the level of collision risk between the vehicle 100 and the neighboring vehicle 200 based on the location information calculated by the neighboring vehicle information device 70 (Lee par. 62). For example, the zone may be partitioned into sub-zones in various shapes like sections A, B, and C as shown in FIG. 4. The collision risk is reduced in order of A, B, and C, and the computing device 30 may compute to reduce the target speed by increasing the subtraction speed according to the level of collision risk in each partitioned zone (Lee par. 63).
Therefore, It would have been obviously to one of ordinary skill in the art before the effective filing date of the claim invention to combine Patil et al., Luo et al. and Stenneth et al. with Lee by comprising the teaching of Lee into the method of Patil et al., Luo et al. and Stenneth et al.. The motivation to combine these arts is provide a simple substitution with plurality of sub-zones according to the level of collision risk between the vehicle and the neighboring vehicle from Lee reference into Patil et al., Luo et al. and Stenneth et al. reference so the results of the substitution would have been predictable the position and distance of the vehicle associated with the plurality of sub-zones.
Regarding claim 18, the combination of Patil et al., Luo et al., Stenneth et al. and Lee disclose The method of claim 37 wherein determining the operating context for the second vehicle includes determining current positions for the first vehicle and the second vehicle, determining trajectories for the first vehicle and the second vehicle, determining a geometry of an environment proximate to the second vehicle, or combinations thereof.
FIG. 1 illustrates an example scanning environment 100 for a group of vehicles that may perform coordinated LiDAR scanning. Environment 100 includes a portion of a roadway 102, on which vehicles 104a, 104b, 104c, and 104d are travelling in a first direction, and on which vehicle 104e is travelling in a second, opposite direction. Closely driving vehicles 104b, 104c, and 104d having the same driving direction (e.g., meaning that the vehicles will not likely be far away from one another shortly/within a threshold amount of time) may be grouped temporarily. Vehicle 104a may not be grouped with vehicles 104b, 104c, and 104d because the distance between vehicle 104a and any one of the vehicles of the group may be greater than a threshold distance, or because the vehicles may be grouped according to a “three nearest neighbors” vehicle grouping parameter (in which a vehicle groups with the two nearest neighbors to the vehicle to form the three nearest neighbors group) and vehicle 104a is not one of the three nearest neighbors (e.g., each of the vehicles in the group are closer to each other vehicle in the group than to vehicle 104a). Vehicle 104e may not be grouped with vehicles 104b, 104c, and 104d because vehicle 104e is travelling in an opposite direction from the grouped vehicles (e.g., meaning that vehicle 104e will likely be far away from the grouped vehicles shortly/within a threshold amount of time). Each vehicle in the group may be communicatively connected to each other member of the group, via individual direct communication links, as illustrated in FIG. 1, and/or via another ad-hoc communication network (Luo et al. par. 17). FIG. 2 shows an example processing flow 200 for performing a coordinated scanning operation in a selected vehicle of a group of vehicles. The processing flow may begin at a discovery phase 202. Each vehicle may set associated vehicle information as being discoverable in order to ensure that the vehicles may be identified to one another when data sharing is to be performed. The identification may include LiDAR status, vehicle position, driving direction, and communication protocols that are supported by the vehicle. Accordingly, in the discovery phase, vehicles may receive and broadcast the above-described discovery information and/or send out/respond to requests for the above-described discovery information in order to identify surrounding vehicles. In some examples, a vehicle may continuously or periodically (e.g., based on a time and/or event-based trigger) broadcast vehicle information, a subset of vehicle information, or an announcement-type message indicating that vehicle information is available upon request. A given vehicle may be in a continuous discovery state (to perform actions for the discovery phase) in some examples, whereas in other examples, a vehicle may enter a discovery state responsive to a condition, such as a trigger for starting a scanning operation or a location- or navigation-based trigger (e.g., the vehicle arriving in a location likely to have multiple vehicles, the vehicle starting on a portion of a path of travel with no turn-offs for a threshold distance, etc.) (Luo et al. par. 19).
According to the cited passages and figures, examiner interpret each host vehicle can treat other vehicles in the group as the second or third vehicle. For example, in the figure 1, vehicle 104d can treat vehicle 104b as the second vehicle and 104c as the third vehicle or vehicle 104c can treat vehicle 104d as the second vehicle and 104b as the third vehicle. They are all sharing their information like vehicle position and trajectory with each other base on the proximity group status.
Regarding claim 19, the combination of Patil et al., Luo et al., Stenneth et al. and Lee disclose The method of claim 37 wherein determining the operating context for the second vehicle includes determining a current environmental condition, determining current road conditions, determining performance factors associated with the second vehicle, or combinations thereof.
FIG. 1 illustrates an example scanning environment 100 for a group of vehicles that may perform coordinated LiDAR scanning. Environment 100 includes a portion of a roadway 102, on which vehicles 104a, 104b, 104c, and 104d are travelling in a first direction, and on which vehicle 104e is travelling in a second, opposite direction. Closely driving vehicles 104b, 104c, and 104d having the same driving direction (e.g., meaning that the vehicles will not likely be far away from one another shortly/within a threshold amount of time) may be grouped temporarily. Vehicle 104a may not be grouped with vehicles 104b, 104c, and 104d because the distance between vehicle 104a and any one of the vehicles of the group may be greater than a threshold distance, or because the vehicles may be grouped according to a “three nearest neighbors” vehicle grouping parameter (in which a vehicle groups with the two nearest neighbors to the vehicle to form the three nearest neighbors group) and vehicle 104a is not one of the three nearest neighbors (e.g., each of the vehicles in the group are closer to each other vehicle in the group than to vehicle 104a). Vehicle 104e may not be grouped with vehicles 104b, 104c, and 104d because vehicle 104e is travelling in an opposite direction from the grouped vehicles (e.g., meaning that vehicle 104e will likely be far away from the grouped vehicles shortly/within a threshold amount of time). Each vehicle in the group may be communicatively connected to each other member of the group, via individual direct communication links, as illustrated in FIG. 1, and/or via another ad-hoc communication network (Luo et al. par. 17). FIG. 2 shows an example processing flow 200 for performing a coordinated scanning operation in a selected vehicle of a group of vehicles. The processing flow may begin at a discovery phase 202. Each vehicle may set associated vehicle information as being discoverable in order to ensure that the vehicles may be identified to one another when data sharing is to be performed. The identification may include LiDAR status, vehicle position, driving direction, and communication protocols that are supported by the vehicle. Accordingly, in the discovery phase, vehicles may receive and broadcast the above-described discovery information and/or send out/respond to requests for the above-described discovery information in order to identify surrounding vehicles. In some examples, a vehicle may continuously or periodically (e.g., based on a time and/or event-based trigger) broadcast vehicle information, a subset of vehicle information, or an announcement-type message indicating that vehicle information is available upon request. A given vehicle may be in a continuous discovery state (to perform actions for the discovery phase) in some examples, whereas in other examples, a vehicle may enter a discovery state responsive to a condition, such as a trigger for starting a scanning operation or a location- or navigation-based trigger (e.g., the vehicle arriving in a location likely to have multiple vehicles, the vehicle starting on a portion of a path of travel with no turn-offs for a threshold distance, etc.) (Luo et al. par. 19). Sensors for scanning the environment may include one or more cameras, LiDAR arrays, and/or other optical sensors for detecting features of the environment surrounding the vehicle (Luo et al. par. 46).
According to the cited passages and figures, examiner interpret the identification as the index information and each vehicle in the group sharing information with each other like lidar status, vehicle position and driving direction. Those information above can be treated as operating context. Also, each host vehicle can treat other vehicles in the group as the second or third vehicle. For example, in the figure 1, vehicle 104d can treat vehicle 104b as the second vehicle and 104c as the third vehicle or vehicle 104c can treat vehicle 104d as the second vehicle and 104b as the third vehicle. They are all sharing their operation information with each other base on the proximity group status.
Regarding claim 29, the combination of Patil et al., Luo et al., Stenneth et al. and Lee disclose The apparatus of claim 39 wherein the at least one processor is further configured to determine the operating context for the second vehicle based at least in part on a current environmental condition, a current road condition, a performance factor associated with the second vehicle, or combinations thereof.
FIG. 1 illustrates an example scanning environment 100 for a group of vehicles that may perform coordinated LiDAR scanning. Environment 100 includes a portion of a roadway 102, on which vehicles 104a, 104b, 104c, and 104d are travelling in a first direction, and on which vehicle 104e is travelling in a second, opposite direction. Closely driving vehicles 104b, 104c, and 104d having the same driving direction (e.g., meaning that the vehicles will not likely be far away from one another shortly/within a threshold amount of time) may be grouped temporarily. Vehicle 104a may not be grouped with vehicles 104b, 104c, and 104d because the distance between vehicle 104a and any one of the vehicles of the group may be greater than a threshold distance, or because the vehicles may be grouped according to a “three nearest neighbors” vehicle grouping parameter (in which a vehicle groups with the two nearest neighbors to the vehicle to form the three nearest neighbors group) and vehicle 104a is not one of the three nearest neighbors (e.g., each of the vehicles in the group are closer to each other vehicle in the group than to vehicle 104a). Vehicle 104e may not be grouped with vehicles 104b, 104c, and 104d because vehicle 104e is travelling in an opposite direction from the grouped vehicles (e.g., meaning that vehicle 104e will likely be far away from the grouped vehicles shortly/within a threshold amount of time). Each vehicle in the group may be communicatively connected to each other member of the group, via individual direct communication links, as illustrated in FIG. 1, and/or via another ad-hoc communication network (Luo et al. par. 17). FIG. 2 shows an example processing flow 200 for performing a coordinated scanning operation in a selected vehicle of a group of vehicles. The processing flow may begin at a discovery phase 202. Each vehicle may set associated vehicle information as being discoverable in order to ensure that the vehicles may be identified to one another when data sharing is to be performed. The identification may include LiDAR status, vehicle position, driving direction, and communication protocols that are supported by the vehicle. Accordingly, in the discovery phase, vehicles may receive and broadcast the above-described discovery information and/or send out/respond to requests for the above-described discovery information in order to identify surrounding vehicles. In some examples, a vehicle may continuously or periodically (e.g., based on a time and/or event-based trigger) broadcast vehicle information, a subset of vehicle information, or an announcement-type message indicating that vehicle information is available upon request. A given vehicle may be in a continuous discovery state (to perform actions for the discovery phase) in some examples, whereas in other examples, a vehicle may enter a discovery state responsive to a condition, such as a trigger for starting a scanning operation or a location- or navigation-based trigger (e.g., the vehicle arriving in a location likely to have multiple vehicles, the vehicle starting on a portion of a path of travel with no turn-offs for a threshold distance, etc.) (Luo et al. par. 19). Sensors for scanning the environment may include one or more cameras, LiDAR arrays, and/or other optical sensors for detecting features of the environment surrounding the vehicle (Luo et al. par. 46).
According to the cited passages and figures, examiner interpret the identification as the index information and each vehicle in the group sharing information with each other like lidar status, vehicle position and driving direction. Those information above can be treated as operating context. Also, each host vehicle can treat other vehicles in the group as the second or third vehicle. For example, in the figure 1, vehicle 104d can treat vehicle 104b as the second vehicle and 104c as the third vehicle or vehicle 104c can treat vehicle 104d as the second vehicle and 104b as the third vehicle. They are all sharing their operation information with each other base on the proximity group status.
Regarding claim 39, the combination of Patil et al., Luo et al., Stenneth et al. and Lee disclose The apparatus of claim 24, wherein the host data comprises an area of interest proximate to the first vehicle and a plurality of sub-areas of the area of interest, wherein the detected object data is organized into the plurality of data groups corresponding to the plurality of sub-areas, determine one or more sub-areas of the plurality of sub-areas with the risk of collision with the first vehicle based on the relevancy distance intersecting the one or more sub-areas;
The computing device may classify a detection zone in which a neighboring vehicle is detected into a plurality of sub-zones according to the level of collision risk based on the location information of the neighboring vehicle and compute the target speed based on the location information of the neighboring vehicle detected in the detection zone (Lee par. 17). As shown in FIG. 4, the computing device 30 may classify the detection zone of the neighboring vehicle 200 into a plurality of sub-zones according to the level of collision risk between the vehicle 100 and the neighboring vehicle 200 based on the location information calculated by the neighboring vehicle information device 70 (Lee par. 62). For example, the zone may be partitioned into sub-zones in various shapes like sections A, B, and C as shown in FIG. 4. The collision risk is reduced in order of A, B, and C, and the computing device 30 may compute to reduce the target speed by increasing the subtraction speed according to the level of collision risk in each partitioned zone (Lee par. 63).
wherein the operating context comprises a relevancy distance associated with the second vehicle, wherein the at least one processor configured to decode the plurality of data groups is further configured to: and decode the one or more data groups of the plurality of data groups corresponding to the one or more sub-areas or discard the one or more data groups of the plurality of data groups not corresponding to the one or more sub-areas.
One advantage of such an approach is that common information may be transmitted on a given set of resources. So the interference is among vehicles that are transmitting common information, which may help control some of the interference caused due to flooding. Thus, in one case for example, a receiving vehicle on a resource may only need to decode one of the transmissions when it is known that they are transmitting common information. The vehicle may therefore have an option to select from the plurality of transmission only one to decode based on any one or more parameters and/or system information such as signal strength, noise value, error rates, proximity, location, transmitting entity, or any other determining factor or combination thereof. For example, the receiving vehicle may select to decode the signal coming from the closest vehicle. In some cases, the resource size may be set to allow for decoding information at low SNR (e.g., 0 dB or lower) (Patil et al. par. 90). It may be generally expected that vehicles that are in proximity will send basic safety messages (BSMs) to each other. Such BSMs may contain location of the vehicle along with expected trajectory. If such BSMs can also contain information from sensors being carried by the vehicles, then any given vehicle can receive such information and decide whether it is the closest to an area, among all the vehicles for which it has received the messages (Patil et al. par. 93). In some cases, only if it is the closest to an area will that vehicle transmit information on the resource associated with that area. This approach helps makes sure that there is less collision of signals from vehicles. There are several variants to this approach, however, that involve more than one vehicle transmitting. For example, instead of the closest, the two closest vehicles transmit information on the resource. As another example, only the closest and the farthest vehicle (within a certain distance) transmitting the information (to have better reuse) (Patil et al. par. 94). Yet another example approach is to have a vehicle (or vehicles) that are closest to an area from a certain direction transmit. For example, vehicles closest to an area from the north/west/east/south side among all other vehicles (for which they have received a BSM) will transmit information about that area Patil et al. par. 95).
According to the cited passages and figures, examiner interpret the system select to decode the signal coming from the closest vehicle since the object or obstacle closest to the vehicle are more relevant to the risk of collision with another vehicle in the proximity distance relative to the vehicle location.
Claim 33 is rejected under 35 U.S.C. 103 as being unpatentable over Patil et al. US 20180082493, in view of Luo et al. US 20190120964, in view of Stenneth et al. US 20220309521 and further in view of Bauchot et al. US 20210216775.
Regarding claim 33, the combination of Patil et al., Luo et al. and Stenneth et al. teach all the limitation in the claim 24.
The combination of Patil et al., Luo et al. and Stenneth et al. do not explicitly teach The apparatus of claim 24,wherein the at least one processor is configured to use object velocity to determine the one or more data groups that are relevant to the risk of collision with the second vehicle each represent a safety risk.
Bauchot et al. teach The apparatus of claim 24,wherein the at least one processor is configured to use object velocity to determine the one or more data groups that are relevant to the risk of collision with the second vehicle each represent a safety risk. (Bauchot et al. US 20210216775 abstract; [0009]-[0016]; [0020]- [0021];figures 1-5;)
The object can include, but not limited to, an automobile, a bicycle, or a pedestrian. Using artificial intelligence, such as machine learning systems, the analytics engine 113 processes the information for the recognized objects and predicts a probability that a risk event exists. A risk event exists when the objects in the video frame poses a safety risk for the vehicle 120. A safety risk can include, but is not limited to, a projected collision between the vehicle 120 and an object or the object obstructing the projected path of the vehicle 120 (Bauchot et al. par 12). In some embodiment, the analytics engine 113 processes multiple video frames to calculate a speed and direction of motion of the object in the video frames. The analytics engine 113 also calculates the speed and direction of the vehicle 120 and uses both sets of data to determine the probability that the risk event exists. For example, the speeds and directions of the object and the vehicle 120 may indicate a potential of a collision (Bauchot et al. par. 13).
Therefore, It would have been obviously to one of ordinary skill in the art before the effective filing date of the claim invention to combine Patil et al., Luo et al. and Stenneth et al. with Bauchot et al. by comprising the teaching of Bauchot et al. into the method of Patil et al., Luo et al. and Stenneth et al.. The motivation to combine these arts is provide decode the video frame to determine a safety risk from Bauchot et al. reference into Patil et al., Luo et al. and Stenneth et al. reference to help user aware of position, speed and heading of the object in the proximity distance to avoid collision.
Claims 36 and 38 are rejected under 35 U.S.C. 103 as being unpatentable over Patil et al. US 20180082493, in view of Luo et al. US 20190120964, in view of Stenneth et al. US 20220309521, in view of Taguchi Seiki JP 2005346333 and further in view of Lee US 20220219690.
Regarding claim 36, the combination of Patil et al., Luo et al., Stenneth et al. and Taguchi Seiki teach all the limitation in the claim 1.
The combination of Patil et al., Luo et al., Stenneth et al. and Taguchi Seiki do not explicitly teach The method of claim 1, wherein the host data comprises an area of interest proximate to the vehicle and a plurality of sub-areas of the area of interest, wherein the detected object data is organized based on the plurality of data groups corresponding to the plurality of sub-areas based on the one or more factors that increase or decrease the risk of collision with the vehicle.
Lee teaches The method of claim 1, wherein the host data comprises an area of interest proximate to the vehicle and a plurality of sub-areas of the area of interest, wherein the detected object data is organized based on the plurality of data groups corresponding to the plurality of sub-areas based on the one or more factors that increase or decrease the risk of collision with the vehicle. (Lee US 20220219690 paragraphs [0017]-[0019]; [0058]-[0067]; figures 1-5;)
The computing device may classify a detection zone in which a neighboring vehicle is detected into a plurality of sub-zones according to the level of collision risk based on the location information of the neighboring vehicle and compute the target speed based on the location information of the neighboring vehicle detected in the detection zone (Lee par. 17). As shown in FIG. 4, the computing device 30 may classify the detection zone of the neighboring vehicle 200 into a plurality of sub-zones according to the level of collision risk between the vehicle 100 and the neighboring vehicle 200 based on the location information calculated by the neighboring vehicle information device 70 (Lee par. 62). For example, the zone may be partitioned into sub-zones in various shapes like sections A, B, and C as shown in FIG. 4. The collision risk is reduced in order of A, B, and C, and the computing device 30 may compute to reduce the target speed by increasing the subtraction speed according to the level of collision risk in each partitioned zone (Lee par. 63).
Therefore, Therefore, It would have been obviously to one of ordinary skill in the art before the effective filing date of the claim invention to combine Patil et al., Luo et al., Stenneth et al. and Taguchi Seiki with Lee by comprising the teaching of Lee into the method of Patil et al., Luo et al., Stenneth et al. and Taguchi Seiki. The motivation to combine these arts is provide a simple substitution with plurality of sub-zones according to the level of collision risk between the vehicle and the neighboring vehicle from Lee reference into Patil et al., Luo et al., Stenneth et al. and Taguchi Seiki reference so the results of the substitution would have been predictable the position and distance of the vehicle associated with the plurality of sub-zones corresponding to the level of vehicle collision risk.
Regarding claim 38, the combination of Patil et al., Luo et al., Stenneth et al., Taguchi Seiki and Lee disclose The apparatus of claim 21, wherein the host data comprises an area of interest proximate to the vehicle and a plurality of sub-areas of the area of interest, wherein the detected object data is organized based on the plurality of data groups corresponding to the plurality of sub-areas based on the one or more factors that increase or decrease the risk of collision with the vehicle.
The computing device may classify a detection zone in which a neighboring vehicle is detected into a plurality of sub-zones according to the level of collision risk based on the location information of the neighboring vehicle and compute the target speed based on the location information of the neighboring vehicle detected in the detection zone (Lee par. 17). As shown in FIG. 4, the computing device 30 may classify the detection zone of the neighboring vehicle 200 into a plurality of sub-zones according to the level of collision risk between the vehicle 100 and the neighboring vehicle 200 based on the location information calculated by the neighboring vehicle information device 70 (Lee par. 62). For example, the zone may be partitioned into sub-zones in various shapes like sections A, B, and C as shown in FIG. 4. The collision risk is reduced in order of A, B, and C, and the computing device 30 may compute to reduce the target speed by increasing the subtraction speed according to the level of collision risk in each partitioned zone (Lee par. 63).
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Patil et al. US 20180082493, in view of Luo et al. US 20190120964, in view of Stenneth et al. US 20220309521, in view of Taguchi Seiki JP 2005346333, in view of Lee US 20220219690 and further in view of Sunil Kumar et al. US 20200209364.
Regarding claim 5, the combination of Patil et al., Luo et al., Stenneth et al., Taguchi Seiki and Lee teach all the limitation in the claim 36.
The combination of Patil et al., Luo et al., Stenneth et al., Taguchi Seiki and Lee do not explicitly teach The method of claim 36 wherein the host data includes a second array of index values associated with position offsets for each of the plurality of sub-areas.
Sunil Kumar et al. teach The method of claim 36 wherein the host data includes a second array of index values associated with position offsets for each of the plurality of sub-areas. (Sunil Kumar et al. US 20200209364 abstract; paragraph [0038]-[0041]; figures 1-5;)
In an embodiment, distance of view of the one or more static objects 303.1 . . . 303.4 for the first predefined view 302.1 may be set in accordance with embodiments of the present disclosure. In an embodiment, the line cluster data determination module 201 may be configured to determine the first line clusters 304.1 . . . 304.4 for each of the one or more static objects 303.1 . . . 303.4, using the reflected points received for the first predefined view 302.1. In an embodiment, a region growing algorithm is implemented around the reflected points to determine the first line clusters 304.1 . . . 304.4. In an embodiment, the first line clusters 304.1 . . . 304.4 for the moving vehicle 301 may be stored as the first line cluster data 207. In an embodiment, the first line clusters 304.1 . . . 304.4 may be determined to identify offset position of the moving vehicle 301 with respect to the one or more static objects 303.1 . . . 303.4 (Sunil Kumar et al. par. 38). In an embodiment, distance of view for the second predefined view 302.2 may be set in accordance with embodiments of the present disclosure. In an embodiment, the line cluster data determination module 201 may be configured to determine second line clusters 305.1 . . . 305.4 corresponding to the first line clusters 304.1 . . . 304.4, for static objects 303.1 . . . 303.4 in the second predefined view 302.2, using the reflected points received for the second predefined view 302.2. In an embodiment, a region growing algorithm is implemented around the reflected points to determine the second line clusters 305.1 . . . 305.4. In an embodiment, the second line clusters 305.1 . . . 305.4 for the moving vehicle 301 may be stored as the second line cluster data 209. In an embodiment, the second line clusters 305.1 . . . 305.4 are determined to identify offset position of the moving vehicle 301 with respect to the one or more static objects 303.1 . . . 303.4 (Sunil Kumar et al. par. 41).
According to the cited passages and figures, examiner interpret the second predefined view 302.2 as a second array index from the moving vehicle are associated with the offset position for multiple location relative to the vehicle 301 as mention in the cited passages above and figure 3.
Therefore, It would have been obviously to one of ordinary skill in the art before the effective filing date of the claim invention to combine Patil et al., Luo et al., Stenneth et al., Taguchi Seiki and Lee with Sunil Kumar et al. by comprising the teaching of Sunil Kumar et al. into the method of Patil et al., Luo et al., Stenneth et al., Taguchi Seiki and Lee. The motivation to combine these arts is to identify offset position of the moving vehicle with respect to the one or more static objects from Sunil Kumar et al. reference into Patil et al., Luo et al., Stenneth et al., Taguchi Seiki and Lee reference to help user aware of the object in the field of view for avoid collision.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Patil et al. US 20180082493, in view of Luo et al. US 20190120964, in view of Stenneth et al. US 20220309521, in view of Lee US 20220219690 and further in view of Sunil Kumar et al. US 20200209364.
Regarding claim 14, the combination of Patil et al., Luo et al., Stenneth et al. and Lee teach all the limitation in the claim 37.
The combination of Patil et al., Luo et al., Stenneth et al. and Lee do not explicitly teach The method of claim 37 wherein the host data includes a second array of index values associated with position offsets for each of the plurality of sub-areas.
Sunil Kumar et al. teach The method of claim 37 wherein the host data includes a second array of index values associated with position offsets for each of the plurality of sub-areas. (Sunil Kumar et al. US 20200209364 abstract; paragraph [0038]-[0041]; figures 1-5;)
In an embodiment, distance of view of the one or more static objects 303.1 . . . 303.4 for the first predefined view 302.1 may be set in accordance with embodiments of the present disclosure. In an embodiment, the line cluster data determination module 201 may be configured to determine the first line clusters 304.1 . . . 304.4 for each of the one or more static objects 303.1 . . . 303.4, using the reflected points received for the first predefined view 302.1. In an embodiment, a region growing algorithm is implemented around the reflected points to determine the first line clusters 304.1 . . . 304.4. In an embodiment, the first line clusters 304.1 . . . 304.4 for the moving vehicle 301 may be stored as the first line cluster data 207. In an embodiment, the first line clusters 304.1 . . . 304.4 may be determined to identify offset position of the moving vehicle 301 with respect to the one or more static objects 303.1 . . . 303.4 (Sunil Kumar et al. par. 38). In an embodiment, distance of view for the second predefined view 302.2 may be set in accordance with embodiments of the present disclosure. In an embodiment, the line cluster data determination module 201 may be configured to determine second line clusters 305.1 . . . 305.4 corresponding to the first line clusters 304.1 . . . 304.4, for static objects 303.1 . . . 303.4 in the second predefined view 302.2, using the reflected points received for the second predefined view 302.2. In an embodiment, a region growing algorithm is implemented around the reflected points to determine the second line clusters 305.1 . . . 305.4. In an embodiment, the second line clusters 305.1 . . . 305.4 for the moving vehicle 301 may be stored as the second line cluster data 209. In an embodiment, the second line clusters 305.1 . . . 305.4 are determined to identify offset position of the moving vehicle 301 with respect to the one or more static objects 303.1 . . . 303.4 (Sunil Kumar et al. par. 41).
According to the cited passages and figures, examiner interpret the second predefined view 302.2 as a second array index from the moving vehicle are associated with the offset position for multiple location relative to the vehicle 301 as mention in the cited passages above and figure 3.
Therefore, It would have been obviously to one of ordinary skill in the art before the effective filing date of the claim invention to combine Patil et al., Luo et al., Stenneth et al. and Lee with Sunil Kumar et al. by comprising the teaching of Sunil Kumar et al. into the method of Patil et al., Luo et al., Stenneth et al. and Lee. The motivation to combine these arts is to identify offset position of the moving vehicle with respect to the one or more static objects from Sunil Kumar et al. reference into Patil et al., Luo et al., Stenneth et al. and Lee reference to help user aware of the object in the field of view for avoid collision.
Response to Arguments
Applicant's arguments filed 03/10/2024 have been fully considered but they are not persuasive. In the remark applicant argues in substance:
Applicant arguments. First, Applicant argues that Patil et al., Luo et al. and Stenneth et al. failed to teach or suggest the amendment “wherein the plurality of data groups are associated with one or more factors that increase or decrease a risk of collision with accident” as recited in the independent claims 1 and 21. Second, Applicant argues that Patil et al., Luo et al. and Stenneth et al. failed to teach or suggest the amendment “decoding one or more data groups of the plurality of data groups that are relevant to a risk of collision with the first vehicle, or discarding the one or more data groups of the plurality of data groups that are not relevant to the risk of collision with the first vehicle” as recited in the independent claims 10 and 24.
Examiner response: First, examiner respectfully submit that the presented arguments are rendered moot in view of the new ground rejection with the new reference Taguchi Seiki necessitated by amendments initiated by applicant in the independent claims 1 and 21. Please see above rejections. Secondly, examiner respectfully submit that Patil et al., Luo et al. and Stenneth et al. do teach “decoding one or more data groups of the plurality of data groups that are relevant to a risk of collision with the first vehicle, or discarding the one or more data groups of the plurality of data groups that are not relevant to the risk of collision with the first vehicle” as recited in the independent claims 10 and 24. For example paragraph 93 of Patil et al. reference teach the vehicle is selected to decode the signal coming from the closest vehicle that pose the potential risk of collision with vehicle. The closest vehicle will share the basic information message with the vehicle location of vehicle along with the expected trajectory. Patil et al. reference disclose the vehicle location along with expected trajectory of the vehicle which is same as the factor that distribute the risk of collision as mention in the paragraphs 112 of the specification. Examiner interpret the location of the vehicle and trajectory of the vehicle within the proximity to another vehicles within the relative distance as illustrate in the figure 8 of Patil et al. reference and figure 1 of Luo et al. reference as the data of the group of the vehicles will distribute the relevant information to the risk of collision. Since arts of the records still read on the independent claims 10 and 24, therefore, the rejection stand for claims 10 and 24. Please see above rejection. The new dependents claims 36-39 are rendered moot in view of the new ground rejection with the new reference Lee.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to THANG D TRAN whose telephone number is (408)918-7546. The examiner can normally be reached Monday - Friday 8:00 am - 5:30 pm (pacific time).
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, Brian A Zimmerman can be reached at 571-272-3059. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/THANG D TRAN/Examiner, Art Unit 2686
/BRIAN A ZIMMERMAN/Supervisory Patent Examiner, Art Unit 2686
Prosecution Insights