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 .
Claims
Claims 1-20 are pending in the application.
Claim Objections
Claim 8 objected to because of the following informalities:”…the processor configured to execute instructions comprising instructions to:” would be better written as: ”…the processor configured to execute instructions, wherein the instructions are to:”
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 is ambiguous because it is unclear from where the control signal is received in line 2. Is the control signal received from multiple vehicles on a road segment? Or is it received from somewhere else in the vehicle, but is based on real-time vehicle sensor data (which is from multiple vehicles on a road segment)? For purposes of examination, the latter is assumed.
(Claims 2-7 are similarly rejected due to their dependence upon claim 1)
Claim 8 is ambiguous because it is unclear from where the control signal is received in line 4. Is the control signal received from multiple vehicles on a road segment? Or is it received from somewhere else in the vehicle, but is based on real-time vehicle sensor data (which is from multiple vehicles on a road segment)? For purposes of examination, the latter is assumed.
(Claims 9-14 are similarly rejected due to their dependence upon claim 8).
Claim 15 is ambiguous because it is unclear from where the control signal is received in line 3. Is the control signal received from multiple vehicles on a road segment? Or is it received from somewhere else in the vehicle, but is based on real-time vehicle sensor data (which is from multiple vehicles on a road segment)? For purposes of examination, the latter is assumed.
(Claims 16-20 are similarly rejected due to their dependence upon claim 15).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2 , 5-9, 12-16, and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over US 9,272,711 Sivaraman, in light of US Pat. 8948995 (Pandita et al., hence Pandita).
As for claim 1, Sivaraman teaches a method implemented by a processor of a vehicle, (Sivaraman: Fig 7 showing a method; Fig. 5; "In a further illustrative embodiment, a congestion-based adaptive cruise control (ACC) system for a vehicle is disclosed, comprising a processor for providing control signals for the vehicle to modify at least one of acceleration, deceleration and braking in the vehicle…” (Col.1 lines 62-66)) comprising:
receiving a control signal generated based on real-time vehicle sensor data from multiple vehicles (Sivaraman: Fig. 7 blocks 704 and 706 obtaining real-time data) on a road (Sivaraman: a “road segment" can be interpreted as the road within a certain distance of the ego-vehicle, both forwards and backwards. See Figs. 4A-4C Sivaraman);
using at least a portion of the control signal to generate an adaptive cruise control parameter that includes [a] headway parameter; (Sivaraman: See Fig. 7, block 710.)
and configuring a cruise control module of the vehicle to use the adaptive cruise control parameter. ("Vehicle traffic sensor arrangement 300 is configured to operate as an ACC system by monitoring and regulating the distance of vehicle 101 to front and rear vehicles to make driving easier for drivers, particularly in congested ("stop-and-go") traffic" (Col. 5, lines 23-27))
Sivaraman does not specifically teach using at least a portion of the control signal to generate an adaptive cruise control parameter that includes a speed parameter, but this is taught by Pandita: (Pandita: "The two most sensitive parameters were found to be the target cruise velocity and headway. The target cruise velocity may be assumed to be the speed limit of the road, and this may be determined by GPS, other positioning system, or otherwise estimated from previous vehicle behavior. Hence, examples of the present invention include a single parameter approach to model optimization, in which only the headway was optimized. It was found to be sufficient to estimate only the headway parameter using the parameter estimation technique, if the target cruise velocity was known or otherwise determined." (Col. 2, lines 45-55))
It would have been obvious to one of ordinary skill in the art at the time of the application to add a speed parameter for controlling the distance between the ego-vehicle and surrounding vehicles, as explained by Pandita, in the system of Sivaraman, which uses a distance parameter. The motivation would be to add a second parameter for further control.
As for claim 2, Sivaraman, as modified by Pandita, teaches wherein the control signal (Sivaraman: see Figs. 6 and 7, which show the development of a control signal; also see explanation in Cols. 9, 10) comprises additional data from roadway sensors indicative of road conditions and traffic density (Sivaraman: traffic data from roadway sensors, see Col. 8 lines 29-34. Traffic data being traffic flow data (which would indicate congestion) and incident data (which can be considered to be “road conditions”, see Col. 8, line 55-56), and wherein the adaptive cruise control parameter is adjusted based on the road conditions and the traffic density. (Sivaraman: That the material gathered by the navigation unit gets used in the system, see Fig.7 and "In decision block 606, vehicle determines if a congestion status is in effect. This determination may be made from navigational data, discussed above in connection with FIG. 6, 10 or may be determined from distance/speed sensor measurements from vehicle 101." Col. 10 lines 8-11.)
As for claim 5, Sivaraman, as modified by Pandita, teaches determining, based on the control signal, a deceleration rate for maintaining the headway parameter under varying traffic conditions, and wherein the headway parameter is adjusted dynamically based on real-time vehicle and traffic data. (Sivaraman: "...and wherein the processor means is configured to generate at least one control signal based on the velocity value to alter the velocity of the vehicle using at least one of acceleration, deceleration and braking to position the vehicle substantially at a mid-point between the front vehicle and rear vehicle. "Col. 1, lines 45-50. That the headway parameter is adjusted dynamically, see Col 7 lines 1-26. That the ACC distances can be modified based on the type of vehicle in front/in back, (which can be considered "traffic data" ), see Col. 7 lines 27-49.)
As for claim 6, Sivaraman, as modified by Pandita, teaches outputting a notification to an occupant of the vehicle when the cruise control module is configured, and wherein the notification includes information regarding the adaptive cruise control parameter being used. (Sivaraman: "Display 110 may be configured to provide visual (as well as audio) indicial from any module in Fig. 1 and may be a configured as a LCD, LED, OLED, or any other suitable display." (Col. 4 lines 39-41); note that Fig. 1 contains a speedometer as well as the results from sensors which, can be measuring distance from the vehicles ahead or behind (See Figs. 4A-4C).)
As for claim 7, Sivaraman, as modified by Pandita, teaches wherein the adaptive cruise control parameter includes a speed limit and a headway value, and wherein the cruise control module is further configured to adjust vehicle acceleration and deceleration to maintain both values. (Pandita: "The two most sensitive parameters were found to be the target cruise velocity and headway. The target cruise velocity may be assumed to be the speed limit of the road, and this may be determined by GPS, other positioning system, or otherwise estimated from previous vehicle behavior. Hence, examples of the present invention include a single parameter approach to model optimization, in which only the headway was optimized. It was found to be sufficient to estimate only the headway parameter using the parameter estimation technique, if the target cruise velocity was known or otherwise determined." (Col. 2, lines 45-55))
As for claim 8, Sivaraman teaches a vehicle, (Sivaraman: Fig. 4A-4C, see Target 101) comprising: a processor, the processor configured to execute instructions comprising instructions to: receive a control signal generated based on real-time vehicle sensor data from multiple vehicles on a road segment (Sivaraman: "In a further illustrative embodiment, a congestion-based adaptive cruise control (ACC) system for a vehicle is disclosed, comprising a processor for providing control signals for the vehicle to modify at least one of acceleration, deceleration and braking in the vehicle; and a sensor arrangement, operatively coupled to the processor, the sensor arrangement being configured to determine (i) a distance value for a front vehicle based on a front vehicle velocity, and (ii) a distance value for a rear vehicle based on a rear vehicle velocity, wherein the processor is configured to process (i) and (ii) from the sensor arrangement to determine a velocity value, and wherein the processor is configured to generate at least one control signal based on the velocity value to position the vehicle between the front vehicle and rear vehicle. (Col.1 lines 62-Col.2 line 8));
use at least a portion of the control signal to generate an adaptive cruise control parameter that includes [a] headway parameter (Sivaraman: See Fig. 7, block 710.);
and configuring a cruise control module of the vehicle to use the adaptive cruise control parameter. ("Vehicle traffic sensor arrangement 300 is configured to operate as an ACC system by monitoring and regulating the distance of vehicle 101 to front and rear vehicles to make driving easier for drivers, particularly in congested ("stop-and-go") traffic" (Col. 5, lines 23-27))
Sivaraman does not specifically teach using at least a portion of the control signal to generate an adaptive cruise control parameter that includes a speed parameter, but this is taught by Pandita: (Pandita: "The two most sensitive parameters were found to be the target cruise velocity and headway. The target cruise velocity may be assumed to be the speed limit of the road, and this may be determined by GPS, other positioning system, or otherwise estimated from previous vehicle behavior. Hence, examples of the present invention include a single parameter approach to model optimization, in which only the headway was optimized. It was found to be sufficient to estimate only the headway parameter using the parameter estimation technique, if the target cruise velocity was known or otherwise determined." (Col. 2, lines 45-55))
It would have been obvious to one of ordinary skill in the art at the time of the application to add a speed parameter for controlling the distance between the ego-vehicle and surrounding vehicles, as explained by Pandita, in the system of Sivaraman, which uses a distance parameter. The motivation would be to add a second parameter for further control.
As for claim 9, Sivaraman, as modified by Pandita, teaches wherein the control signal (Sivaraman: see Figs. 6 and 7, which show the development of a control signal; also see explanation in Cols. 9, 10) comprises additional data from roadway sensors indicative of road conditions and traffic density (Sivaraman: traffic data from roadway sensors, see Col. 8 lines 29-34. Traffic data being traffic flow data (which would indicate congestion) and incident data (which can be considered to be “road conditions”, see Col. 8, line 55-56), and wherein the adaptive cruise control parameter is adjusted based on the road conditions and the traffic density. (Sivaraman: That the material gathered by the navigation unit gets used in the system, see Fig.7 and "In decision block 606, vehicle determines if a congestion status is in effect. This determination may be made from navigational data, discussed above in connection with FIG. 6, 10 or may be determined from distance/speed sensor measurements from vehicle 101." Col. 10 lines 8-11.)
As for claim 12, Sivaraman, as modified by Pandita, teaches determining, based on the control signal, a deceleration rate for maintaining the headway parameter under varying traffic conditions, and wherein the headway parameter is adjusted dynamically based on real-time vehicle and traffic data. (Sivaraman: "...and wherein the processor means is configured to generate at least one control signal based on the velocity value to alter the velocity of the vehicle using at least one of acceleration, deceleration and braking to position the vehicle substantially at a mid-point between the front vehicle and rear vehicle. "Col. 1, lines 45-50. That the headway parameter is adjusted dynamically, see Col 7 lines 1-26. That the ACC distances can be modified based on the type of vehicle in front/in back, (which can be considered "traffic data" ), see Col. 7 lines 27-49.)
As for claim 13, Sivaraman, as modified by Pandita, teaches outputting a notification to an occupant of the vehicle when the cruise control module is configured, and wherein the notification includes information regarding the adaptive cruise control parameter being used. (Sivaraman: "Display 110 may be configured to provide visual (as well as audio) indicial from any module in Fig. 1 and may be a configured as a LCD, LED, OLED, or any other suitable display." (Col. 4 lines 39-41); note that Fig. 1 contains a speedometer as well as the results from sensors which, can be measuring distance from the vehicles ahead or behind (See Figs. 4A-4C).)
As for claim 14, Sivaraman, as modified by Pandita, teaches wherein the adaptive cruise control parameter includes a speed limit and a headway value, and wherein the cruise control module is further configured to adjust vehicle acceleration and deceleration to maintain both values. (Pandita: "The two most sensitive parameters were found to be the target cruise velocity and headway. The target cruise velocity may be assumed to be the speed limit of the road, and this may be determined by GPS, other positioning system, or otherwise estimated from previous vehicle behavior. Hence, examples of the present invention include a single parameter approach to model optimization, in which only the headway was optimized. It was found to be sufficient to estimate only the headway parameter using the parameter estimation technique, if the target cruise velocity was known or otherwise determined." (Col. 2, lines 45-55))
As for claim 15 Sivaraman teaches a non-transitory computer-readable medium storing instructions which, when executed by a processor of a vehicle, cause the processor to perform operations comprising: (Sivaraman: "The disclosed embodiments may be implemented, in some cases, in hardware, firmware, software, or any tangibly-embodied combination thereof. The disclosed embodiments may also be implemented as instructions carried by or stored on one or more non-transitory machine-readable (e.g., computer- readable) storage medium, which may be read and executed by one or more processors. A machine-readable storage medium may be embodied as any storage device, mechanism, or other physical structure for storing or transmitting information in a form readable by a machine (e.g., a volatile or non-volatile memory, a media disc, or other media device)." Col. 3, lines 4-15; processors also mentioned (Col. 1 lines 62-Col. 2 line 8) )
receive a control signal generated based on real-time vehicle sensor data from multiple vehicles on a road segment (Sivaraman: "In a further illustrative embodiment, a congestion-based adaptive cruise control (ACC) system for a vehicle is disclosed, comprising a processor for providing control signals for the vehicle to modify at least one of acceleration, deceleration and braking in the vehicle; and a sensor arrangement, operatively coupled to the processor, the sensor arrangement being configured to determine (i) a distance value for a front vehicle based on a front vehicle velocity, and (ii) a distance value for a rear vehicle based on a rear vehicle velocity, wherein the processor is configured to process (i) and (ii) from the sensor arrangement to determine a velocity value, and wherein the processor is configured to generate at least one control signal based on the velocity value to position the vehicle between the front vehicle and rear vehicle. (Col.1 lines 62-Col.2 line 8));
use at least a portion of the control signal to generate an adaptive cruise control parameter that includes [a] headway parameter (Sivaraman: See Fig. 7, block 710.);
and configuring a cruise control module of the vehicle to use the adaptive cruise control parameter. ("Vehicle traffic sensor arrangement 300 is configured to operate as an ACC system by monitoring and regulating the distance of vehicle 101 to front and rear vehicles to make driving easier for drivers, particularly in congested ("stop-and-go") traffic" (Col. 5, lines 23-27))
Sivaraman does not specifically teach using at least a portion of the control signal to generate an adaptive cruise control parameter that includes a speed parameter, but this is taught by Pandita: (Pandita: "The two most sensitive parameters were found to be the target cruise velocity and headway. The target cruise velocity may be assumed to be the speed limit of the road, and this may be determined by GPS, other positioning system, or otherwise estimated from previous vehicle behavior. Hence, examples of the present invention include a single parameter approach to model optimization, in which only the headway was optimized. It was found to be sufficient to estimate only the headway parameter using the parameter estimation technique, if the target cruise velocity was known or otherwise determined." (Col. 2, lines 45-55))
It would have been obvious to one of ordinary skill in the art at the time of the application to add a speed parameter for controlling the distance between the ego-vehicle and surrounding vehicles, as explained by Pandita, in the system of Sivaraman, which uses a distance parameter. The motivation would be to add a second parameter for further control.
As for claim 16, Sivaraman, as modified by Pandita, teaches wherein the control signal (Sivaraman: see Figs. 6 and 7, which show the development of a control signal; also see explanation in Cols. 9, 10) comprises additional data from roadway sensors indicative of road conditions and traffic density (Sivaraman: traffic data from roadway sensors, see Col. 8 lines 29-34. Traffic data being traffic flow data (which would indicate congestion) and incident data (which can be considered to be “road conditions”, see Col. 8, line 55-56), and wherein the adaptive cruise control parameter is adjusted based on the road conditions and the traffic density. (Sivaraman: That the material gathered by the navigation unit gets used in the system, see Fig.7 and "In decision block 606, vehicle determines if a congestion status is in effect. This determination may be made from navigational data, discussed above in connection with FIG. 6, 10 or may be determined from distance/speed sensor measurements from vehicle 101." Col. 10 lines 8-11.)
As for claim 19, Sivaraman, as modified by Pandita, teaches determining, based on the control signal, a deceleration rate for maintaining the headway parameter under varying traffic conditions, and wherein the headway parameter is adjusted dynamically based on real-time vehicle and traffic data. (Sivaraman: "...and wherein the processor means is configured to generate at least one control signal based on the velocity value to alter the velocity of the vehicle using at least one of acceleration, deceleration and braking to position the vehicle substantially at a mid-point between the front vehicle and rear vehicle. "Col. 1, lines 45-50. That the headway parameter is adjusted dynamically, see Col 7 lines 1-26. That the ACC distances can be modified based on the type of vehicle in front/in back, (which can be considered "traffic data" ), see Col. 7 lines 27-49.)
As for claim 20, Sivaraman, as modified by Pandita, teaches outputting a notification to an occupant of the vehicle when the cruise control module is configured, and wherein the notification includes information regarding the adaptive cruise control parameter being used. (Sivaraman: "Display 110 may be configured to provide visual (as well as audio) indicial from any module in Fig. 1 and may be a configured as a LCD, LED, OLED, or any other suitable display." (Col. 4 lines 39-41); note that Fig. 1 contains a speedometer as well as the results from sensors which, can be measuring distance from the vehicles ahead or behind (See Figs. 4A-4C).)
Claims 3-4 are rejected under 35 U.S.C. 103 as being unpatentable over Sivaraman in light Pandita as applied to claim 1 above, and further in view of CN107499262 (Liang).
As for claim 3, neither Sivaraman nor Pandita specifically teach generating the control signal using a machine-learning model trained to receive traffic data that includes at least one of traffic density, a distance between the vehicle and the road segment, a distance between the vehicle and a congested location, data indicative of traffic speed, and respective speeds of other vehicles proximate to the vehicle, wherein the machine-learning model outputs the adaptive cruise control parameter used to adjust a speed of the vehicle based on the received control signal. However, Liang teaches
generating the control signal using a machine-learning model trained to receive traffic data that includes at least one of traffic density, a distance between the vehicle and the road segment, a distance between the vehicle and a congested location, data indicative of traffic speed, and respective speeds of other vehicles proximate to the vehicle, (Liang: sensors providing targets in front of vehicles ([0018]), V2X data: "The aforementioned vehicle-to-everything (V2X) communication uses wireless technology to allow the vehicle to obtain location, speed, distance, orientation, and status information from other surrounding vehicles, basic transportation facilities, road signs, speed limit signs, pedestrians and their wearable devices, and wireless transmission equipment." [0020]. Data fusion model fusing data from multiple sensors and then sending the fused data to the machine learning and decision control module.([0029]))
wherein the machine-learning model outputs the adaptive cruise control parameter used to adjust a speed of the vehicle based on the received control signal. (Liang: "The machine learning and decision control module integrates the fused data with the vehicle's operating status parameters and driver intent parameters, and feeds these parameters into a deep learning neural network. This combines the ACC/AEB control algorithm with the deep learning neural network, enabling self-learning, self-reasoning, self-correction, and continuous iteration to obtain optimal parameters for collision warning time, collision warning time reciprocal, braking deceleration, braking force, and acceleration. Intelligently based on the relevant combinations of collision warning time (TTC) times, it determines the various operating states and intervention timings of ACC and AEB, and sends the control parameters to the actuators in real time to execute the relevant actions."[0015])
It would have been obvious to one of ordinary skill in the art at the time of the application to add a machine learning and decision control module, as outlined in Liang, to the system of Sivaraman. The motivation would be, as Liang mentions ([0011]), to enable the ACC system to fully and intelligently adapt to the driving environment and have the ability to learn and correct itself in special scenarios and operating conditions.
As for claim 4, Sivaraman, in light of Pandita and in light of Liang, teaches wherein the cruise control module adjusts the adaptive cruise control parameter based on at least one of real-time traffic conditions or sensor data indicative of road gradient and surface type. (Liang: "The inputs to the machine learning and decision control module also include the vehicle's slip ratio and road slope and curvature information provided by the high-precision map, and the adhesion coefficient of the road surface is estimated through a CNN network in order to calculate the optimal driving force and the optimal braking force or deceleration."([0031])
Claims 10-11 are rejected under 35 U.S.C. 103 as being unpatentable over Sivaraman in light Pandita as applied to claim 8 above, and further in view of Liang.
As for claim 10, neither Sivaraman nor Pandita specifically teach generating the control signal using a machine-learning model trained to receive traffic data that includes at least one of traffic density, a distance between the vehicle and the road segment, a distance between the vehicle and a congested location, data indicative of traffic speed, and respective speeds of other vehicles proximate to the vehicle, wherein the machine-learning model outputs the adaptive cruise control parameter used to adjust a speed of the vehicle based on the received control signal. However, Liang teaches
generating the control signal using a machine-learning model trained to receive traffic data that includes at least one of traffic density, a distance between the vehicle and the road segment, a distance between the vehicle and a congested location, data indicative of traffic speed, and respective speeds of other vehicles proximate to the vehicle, (Liang: sensors providing targets in front of vehicles ([0018]), V2X data: "The aforementioned vehicle-to-everything (V2X) communication uses wireless technology to allow the vehicle to obtain location, speed, distance, orientation, and status information from other surrounding vehicles, basic transportation facilities, road signs, speed limit signs, pedestrians and their wearable devices, and wireless transmission equipment." [0020]. Data fusion model fusing data from multiple sensors and then sending the fused data to the machine learning and decision control module.([0029]))
wherein the machine-learning model outputs the adaptive cruise control parameter used to adjust a speed of the vehicle based on the received control signal. (Liang: "The machine learning and decision control module integrates the fused data with the vehicle's operating status parameters and driver intent parameters, and feeds these parameters into a deep learning neural network. This combines the ACC/AEB control algorithm with the deep learning neural network, enabling self-learning, self-reasoning, self-correction, and continuous iteration to obtain optimal parameters for collision warning time, collision warning time reciprocal, braking deceleration, braking force, and acceleration. Intelligently based on the relevant combinations of collision warning time (TTC) times, it determines the various operating states and intervention timings of ACC and AEB, and sends the control parameters to the actuators in real time to execute the relevant actions."[0015])
It would have been obvious to one of ordinary skill in the art at the time of the application to add a machine learning and decision control module, as outlined in Liang, to the system of Sivaraman. The motivation would be, as Liang mentions ([0011]), to enable the ACC system to fully and intelligently adapt to the driving environment and have the ability to learn and correct itself in special scenarios and operating conditions.
As for claim 11, Sivaraman, in light of Pandita and in light of Liang, teaches wherein the cruise control module adjusts the adaptive cruise control parameter based on at least one of real-time traffic conditions or sensor data indicative of road gradient and surface type. (Liang: "The inputs to the machine learning and decision control module also include the vehicle's slip ratio and road slope and curvature information provided by the high-precision map, and the adhesion coefficient of the road surface is estimated through a CNN network in order to calculate the optimal driving force and the optimal braking force or deceleration."([0031])
Claims 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Sivaraman in light Pandita as applied to claim 15 above, and further in view of Liang.
As for claim 17, neither Sivaraman nor Pandita specifically teach generating the control signal using a machine-learning model trained to receive traffic data that includes at least one of traffic density, a distance between the vehicle and the road segment, a distance between the vehicle and a congested location, data indicative of traffic speed, and respective speeds of other vehicles proximate to the vehicle, wherein the machine-learning model outputs the adaptive cruise control parameter used to adjust a speed of the vehicle based on the received control signal. However, Liang teaches
generating the control signal using a machine-learning model trained to receive traffic data that includes at least one of traffic density, a distance between the vehicle and the road segment, a distance between the vehicle and a congested location, data indicative of traffic speed, and respective speeds of other vehicles proximate to the vehicle, (Liang: sensors providing targets in front of vehicles ([0018]), V2X data: "The aforementioned vehicle-to-everything (V2X) communication uses wireless technology to allow the vehicle to obtain location, speed, distance, orientation, and status information from other surrounding vehicles, basic transportation facilities, road signs, speed limit signs, pedestrians and their wearable devices, and wireless transmission equipment." [0020]. Data fusion model fusing data from multiple sensors and then sending the fused data to the machine learning and decision control module.([0029]))
wherein the machine-learning model outputs the adaptive cruise control parameter used to adjust a speed of the vehicle based on the received control signal. (Liang: "The machine learning and decision control module integrates the fused data with the vehicle's operating status parameters and driver intent parameters, and feeds these parameters into a deep learning neural network. This combines the ACC/AEB control algorithm with the deep learning neural network, enabling self-learning, self-reasoning, self-correction, and continuous iteration to obtain optimal parameters for collision warning time, collision warning time reciprocal, braking deceleration, braking force, and acceleration. Intelligently based on the relevant combinations of collision warning time (TTC) times, it determines the various operating states and intervention timings of ACC and AEB, and sends the control parameters to the actuators in real time to execute the relevant actions."[0015])
It would have been obvious to one of ordinary skill in the art at the time of the application to add a machine learning and decision control module, as outlined in Liang, to the system of Sivaraman. The motivation would be, as Liang mentions ([0011]), to enable the ACC system to fully and intelligently adapt to the driving environment and have the ability to learn and correct itself in special scenarios and operating conditions.
As for claim 18, Sivaraman, in light of Pandita and in light of Liang, teaches wherein the cruise control module adjusts the adaptive cruise control parameter based on at least one of real-time traffic conditions or sensor data indicative of road gradient and surface type. (Liang: "The inputs to the machine learning and decision control module also include the vehicle's slip ratio and road slope and curvature information provided by the high-precision map, and the adhesion coefficient of the road surface is estimated through a CNN network in order to calculate the optimal driving force and the optimal braking force or deceleration."([0031])
Conclusion
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/TANYA C SIENKO/ Examiner, Art Unit 3664
/KITO R ROBINSON/ Supervisory Patent Examiner, Art Unit 3664