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 . This office action is in response to an application filed on 06/04/2025. The applicant does not submit an Information Disclosure Statement. The applicant does not make a claim for Domestic priority. The applicant makes a claim for Foreign priority to an application filed on 10/30/2024.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1 – 17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea of a mental concept of evaluation without significantly more. The claims are evaluated with respect to the MPEP and to the 2019 Subject Matter Guidance. Example 40 is used a reference for the analysis of the claims.
STEP 1
The claims recite a method for predicting a path of a vehicle, an apparatus for predicting a path of a vehicle, and a non-transitory computer readable medium containing. The claims are directed to the four statutory authorized claim types. Therefore, the claims pass Step 1.
STEP 2A PRONG I
Independent claim 1 is reproduced below identifying the various features of the claim. The analysis applies to the other independent claims.
Claim 1
A method for predicting a path of a vehicle, the method comprising:
acquiring, by at least one processor of the vehicle, driving information of the vehicle, wherein the driving information comprises at least one of a velocity, an acceleration, a steering angle, a steering angle velocity, a heading angle, a yaw rate, a stepped amount of an accelerator pedal and a brake pedal, or a gear shift setting of the vehicle; (pre solution activity)
determining, by the at least one processor, a longitudinal driving intent of a driver based on the driving information; (mental concept of evaluation)
determining, by the at least one processor, a lateral driving intent of the driver based on the driving information; (mental concept of evaluation)
generating, by the at least one processor, a velocity profile of the vehicle based on the longitudinal driving intent and the lateral driving intent; (mental concept of evaluation)
generating, by the at least one processor, a curvature profile of the vehicle based on the longitudinal driving intent and the lateral driving intent; (mental concept of evaluation) and
determining, by the at least one processor, a predicted path of the vehicle based on the velocity profile and the curvature profile. (mental concept of evaluation)
The clause does not identify what constitutes a profile or the purpose of the evaluation of the determination of intent. Therefore, a driver or operator of a vehicle is able to observe the surroundings and anticipate intention of drivers in the vicinity. The claim does not make a claim the invention is an autonomous vehicle. The dependent claims are further evaluation operations where some perform mathematical operations. Ultimately, the claims do not identify what occurs based upon the evaluation.
Therefore, with respect to the MPEP 2106.07 the inventive concept is directed to guessing the path of a vehicle in close proximity. Under MPEP 2106.04(a)(2)(III) the operations may be performed mentally and are performed daily by drivers operating their vehicles and trying to prevent a collision.
With respect to the 2019 Guidance, example 40 shows that when an evaluation occurs the objective and result of the objective must be identified. In this case, the claims do not identify what occurs based upon the evaluation.
Therefore, the claims fail Step 2A Prong I.
STEP 2A PRONG II
This judicial exception is not integrated into a practical application because the claims fail to satisfy the features of MPEP 2106.04(d)(1,2). The claims fail to show how the invention is directed to a new or improved aspect of the art. The claims are directed to an evaluation and thus fail step 2A prong II.
STEP 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the claims fail MPEP 2106.05(a-h). The claims fail to show how the invention is applied to either displaying a warning or executing the anti-collision safety operations.
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 2 – 7 and 12 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. The claims contain the word “if” which is a conditional term, the claims must be claimed positively or negatively under MPEP 2173 and 2173.05(i). The “if” is neither and therefore renders the claims indefinite.
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 - 17 are rejected under 35 U.S.C. 103 as being unpatentable over Abrash US 2023/0103248 in view of Hong US 2020/0064850.
As per claim 1, A method for predicting a path of a vehicle, the method comprising:
acquiring, by at least one processor of the vehicle, driving information of the vehicle, wherein the driving information comprises at least one of a velocity, an acceleration, a steering angle, a steering angle velocity, a heading angle, a yaw rate, a stepped amount of an accelerator pedal and a brake pedal, or a gear shift setting of the vehicle; (Abrash paragraph 0040 discloses, “The vehicle's ADAS/autonomous driving system may track and learn driving patterns for the host vehicle, including historical steering angle data, steering speed, vehicle speed, yaw rate, lateral acceleration, longitudinal acceleration, etc., to predict driver intent.”)
determining, by the at least one processor, a longitudinal driving intent of a driver based on the driving information; (Abrash paragraph 0040 discloses, “The vehicle's ADAS/autonomous driving system may track and learn driving patterns for the host vehicle, including historical steering angle data, steering speed, vehicle speed, yaw rate, lateral acceleration, longitudinal acceleration, etc., to predict driver intent.”)
determining, by the at least one processor, a lateral driving intent of the driver based on the driving information; (Abrash paragraph 0040 discloses, “The vehicle's ADAS/autonomous driving system may track and learn driving patterns for the host vehicle, including historical steering angle data, steering speed, vehicle speed, yaw rate, lateral acceleration, longitudinal acceleration, etc., to predict driver intent.”)
generating, by the at least one processor, a velocity profile of the vehicle based on the longitudinal driving intent and the lateral driving intent; (Abrash paragraph 0014 discloses, “As yet another option, the target dynamics information may include location data, distance data, speed data, trajectory data, etc. of the target vehicle. In this instance, the electronic controller may communicate with the sensor array to receive lane line data for the lane of the road segment. In this instance, estimating the predicted lane assignment for the target vehicle may include comparing the target dynamics information and the lane line data.”) and (Hong paragraph 0039 teaches, “In another aspect, the intent prediction module 233 includes a model for each different maneuver classifiable by movement classifier 232. Based on the received selected maneuver, intent prediction module 233 can identify the corresponding appropriate model.”)
generating, by the at least one processor, a curvature profile of the vehicle based on the longitudinal driving intent and the lateral driving intent; (Abrash paragraph 0038 discloses, “The host reference frame may be a vehicle-calibrated spatial frame of reference with a coordinate system that serves to describe the position of objects relative to the host and whose origin, orientation, and scale may be specified by a set of reference points. The received map data is aligned with respect to the host reference frame, e.g., locating the host vehicle between lane boundary lines and, if appropriate, placing a host reference frame origin on or adjacent a lane centerline. Once in the host vehicle reference frame, lane polynomial information such as width, host vehicle offset, heading, curvature, etc., may be derived for each of the available lanes on a road segment across which the host vehicle travels.”) and
determining, by the at least one processor, a predicted path of the vehicle based on the velocity profile and the curvature profile. (Hong paragraph 0019 teaches, “At the host mobile robot, sensors collect sensor data used to identify and track surrounding robots/vehicles. Motion analysis algorithms can use the sensor data to predict if another robot/vehicle is likely to move laterally (e.g., between lanes) into the path of the host mobile robot, for example, when the other robot/vehicle is navigating a curve or is zigzagging. Motion analysis algorithms can also use sensor data to predict if another robot/vehicle is likely to move longitudinally (e.g., in the same lane) into the path of the host mobile robot, for example, when the other robot/vehicle is accelerating/decelerating rapidly or tailgating.”)
Abrash discloses an automated driving system and control logic for lane localization of target objects in mapped environments. Abrash does not disclose creating velocity profiles for observed target vehicles. Hong teaches of creating velocity profiles for observed target vehicles. Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to incorporate the teachings of Hong et.al. into the invention of Abrash. Such incorporation is motivated by the need to ensure collision doesn’t occur.
As per claim 2, The method of claim 1, wherein determining the longitudinal driving intent comprises determining that the longitudinal driving intent is a first longitudinal driving intent if a magnitude of the acceleration of the vehicle is greater than a predetermined magnitude of acceleration, wherein the first longitudinal driving intent is that the driver intends to accelerate or decelerate rapidly. (Hong paragraph 0019 teaches, “Motion analysis algorithms can also use sensor data to predict if another robot/vehicle is likely to move longitudinally (e.g., in the same lane) into the path of the host mobile robot, for example, when the other robot/vehicle is accelerating/decelerating rapidly or tailgating.”)
Abrash discloses an automated driving system and control logic for lane localization of target objects in mapped environments. Abrash does not disclose creating velocity profiles for observed target vehicles. Hong teaches of creating velocity profiles for observed target vehicles. Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to incorporate the teachings of Hong et.al. into the invention of Abrash. Such incorporation is motivated by the need to ensure collision doesn’t occur.
As per claim 3, The method of claim 2, wherein determining the longitudinal driving intent further comprises:
determining whether the velocity of the vehicle is equal to or lower than a predetermined velocity if the magnitude of the acceleration of the vehicle is equal to or lower than the predetermined magnitude of acceleration; (Abrash paragraph 0040) and
determining that the longitudinal driving intent is a second longitudinal driving intent if the velocity of the vehicle is equal to or lower than the predetermined velocity, (Abrash paragraph 0040) and
determining that the longitudinal driving intent is a third longitudinal driving intent if the velocity of the vehicle is greater than the predetermined velocity, wherein the second longitudinal driving intent is that the driver intends to drive at a low velocity, and the third longitudinal driving intent is that the driver intends to drive at a non-low velocity. (Abrash paragraph 0040)
As per claim 4, The method of claim 3, wherein determining the longitudinal driving intent further comprises determining that the longitudinal driving intent is a fourth longitudinal driving intent if the gear shift setting of the vehicle is to D gear or R gear and there is no input from the accelerator pedal or the brake pedal of the vehicle, wherein the fourth longitudinal driving intent is that the driver intends to creep drive. (Hong paragraph 0036 teaches, “if the mobile robot 201 reverses direction, a front facing sensor can become a rear facing sensor and vice versa.”)
Abrash discloses an automated driving system and control logic for lane localization of target objects in mapped environments. Abrash does not disclose creating velocity profiles for observed target vehicles. Hong teaches of creating velocity profiles for observed target vehicles. Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to incorporate the teachings of Hong et.al. into the invention of Abrash. Such incorporation is motivated by the need to ensure collision doesn’t occur.
As per claim 5, The method of claim 4, wherein determining the lateral driving intent comprises:
determining that the lateral driving intent is a first lateral driving intent if the steering angle of the vehicle is equal to or less than a first predetermined steering angle or if the steering angle velocity of the vehicle is equal to or less than a first predetermined steering angle velocity; (Abrash paragraph 0040)
determining that the lateral driving intent is a second lateral driving intent if the steering angle of the vehicle is greater than a second predetermined steering angle or the steering angle velocity of the vehicle is greater than a second predetermined steering angle velocity; (Abrash paragraph 0040) and
determining that the lateral driving intent is a third lateral driving intent if the lateral driving intent is not the first lateral driving intent or the second lateral driving intent, (Abrash paragraph 0040)
wherein the first lateral driving intent is that the driver intends to make a gentle change of route, and the second lateral driving intent is that the driver intends to make an abrupt change of route. (Abrash paragraph 0040)
As per claim 6, The method of claim 5, wherein determining the lateral driving intent further comprises:
if the lateral driving intent is detennined to be the first lateral driving intent, determining whether the yaw rate of the vehicle is opposite in sign to the steering angle of the vehicle; (Abrash paragraph 0040) and
if the yaw rate of the vehicle is opposite in sign to the steering angle of the vehicle, determining that the lateral driving intent is a fourth lateral driving intent. (Abrash paragraph 0040)
As per claim 7, The method of claim 6, wherein determining the lateral driving intent further comprises:
if the lateral driving intent is determined to be the second lateral driving intent, determining whether the steering angle of the vehicle is opposite in sign to the steering angle velocity of the vehicle; (Abrash paragraph 0040) and
if the steering angle of the vehicle is opposite in sign to the steering angle velocity of the vehicle, determining that the lateral driving intent is a fifth lateral driving intent. (Abrash paragraph 0040)
As per claim 8, The method of claim 7, wherein generating the velocity profile comprises:
generating an initial velocity profile based on the longitudinal driving intent; (Abrash paragraph 0040) and
adjusting the initial velocity profile based on the lateral driving intent. (Hong paragraph 0049 teaches, “The method 300 includes predicting the future movement of the object based on a model corresponding to the selected maneuver (block 303). For example, models 271 can include a model corresponding to each maneuver in the maneuver set 224, including model 272 corresponding to the maneuver 224B. The intent prediction module 233 can access the model 272 from the models 271. The intent prediction module 233 can formulate the predicted movement intent 282 of the object 221A based on the model 272.”)
discloses an automated driving system and control logic for lane localization of target objects in mapped environments. Abrash does not disclose creating velocity profiles for observed target vehicles. Hong teaches of creating velocity profiles for observed target vehicles. Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to incorporate the teachings of Hong et.al. into the invention of Abrash. Such incorporation is motivated by the need to ensure collision doesn’t occur.
As per claim 9, The method of claim 8, wherein the velocity profile comprises an acceleration section, deceleration section and a constant velocity section, and wherein generating the velocity profile based on the longitudinal driving intent comprises determining a length of each of the acceleration section, deceleration section and the constant velocity section based on the longitudinal driving intent. (Abrash paragraph 0014 discloses, “the target dynamics information may include location data, distance data, speed data, trajectory data, etc. of the target vehicle. In this instance, the electronic controller may communicate with the sensor array to receive lane line data for the lane of the road segment. In this instance, estimating the predicted lane assignment for the target vehicle may include comparing the target dynamics information and the lane line data.” And paragraph 0040)
As per claim 10, The method of claim 9, wherein adjusting the initial velocity profile based on the lateral driving intent comprises adjusting the length of each of the acceleration section and deceleration section according to the lateral driving intent. (Abrash paragraph 0014 and 0040)
As per claim 11, The method of claim 7, wherein generating the curvature profile comprises: generating an initial curvature profile based on the lateral driving intent; and adjusting the initial curvature profile based on the longitudinal driving intent. (Hong paragraph 0065 teaches, “Control input-based values can be characterized using a threshold curve based on vehicle speed. FIG. 11 illustrates a threshold curve graph 1100 of mean inputs relative to speed. As depicted, the graph 1100 defines the threshold curve 1103 for mean inputs 1102 relative to speed 1101. The mean inputs 1102 under the threshold curve 1103 for speed 1101 indicate probable safe operation. On the other hand, the mean inputs 1102 over the threshold curve 1103 for speed 1101 indicate possible unsafe operation. For example, indicated by the shaded area, the mean inputs 1102 exceed the threshold curve 1103 at a current speed 1104 for a set of inputs.” And paragraph 0073 teaches, “A mobile robot can utilize the lateral acceleration of surrounding robots to monitor the curve negotiation. To evaluate the lateral acceleration of the surrounding robots, reference lateral accelerations can be determined. The referenced lateral acceleration is determined from the road curvature radius and speed limit. Road curvatures can be provided by sensors (e.g., the environmental sensors 202) or vehicle-to-infrastructure communication. For example, the cameras 206 looking ahead can provide road curvature as a cubic polynomial equation after image processing. Based on the curvature information and lane width, the curved lane is reconstructed as the points {M}.sub.i=1.sup.N in FIG. 6.”)
Abrash discloses an automated driving system and control logic for lane localization of target objects in mapped environments. Abrash does not disclose creating velocity profiles for observed target vehicles. Hong teaches of creating velocity profiles for observed target vehicles. Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to incorporate the teachings of Hong et.al. into the invention of Abrash. Such incorporation is motivated by the need to ensure collision doesn’t occur.
As per claim 12, The method of claim 11, wherein the initial curvature profile is in a form of a linear function if the lateral driving intent is determined to be the first lateral driving intent, in a form of a constant function if the lateral driving intent is determined to be the second or the third lateral driving intent, in a form of a quadratic function if the lateral driving intent is determined to be the fourth lateral driving intent, and in a form of a step function if the lateral driving intent is determined to be the fifth lateral driving intent. (Abrash paragraph 0014 and 0040)
As per claim 13, The method of claim 12, wherein adjusting the curvature profile based on the longitudinal driving intent comprises multiplying the curvature profile by a correction factor, wherein the correction factor is a value between 0 and 1 and is determined based on the longitudinal driving intent. (Abrash paragraph 0014 and 0040)
As per claim 14, An apparatus for predicting a path of a vehicle, comprising:
at least one memory configured to store instructions; (Abrash paragraph 0035 discloses, “an algorithm that corresponds to processor-executable instructions that are stored, for example, in main or auxiliary or remote memory (e.g., memory device 38 of FIG. 1), and executed, for example, by an electronic controller, processing unit, logic circuit, or other module or device or network of modules/devices (e.g., CPU 36 and/or cloud computing service 24 of”) and
at least one processor, wherein the at least one processor executes the instructions for causing the at least one processor to:
acquire driving information of the vehicle, wherein the driving information comprises at least one of a velocity, an acceleration, a steering angle, a steering angle velocity, a heading angle, a yaw rate, a stepped amount of an accelerator pedal and a brake pedal, or a gear shift setting of the vehicle; (Abrash paragraph 0040 discloses, “The vehicle's ADAS/autonomous driving system may track and learn driving patterns for the host vehicle, including historical steering angle data, steering speed, vehicle speed, yaw rate, lateral acceleration, longitudinal acceleration, etc., to predict driver intent.”)
determine a longitudinal driving intent of a driver based on the driving information; (Abrash paragraph 0040 discloses, “The vehicle's ADAS/autonomous driving system may track and learn driving patterns for the host vehicle, including historical steering angle data, steering speed, vehicle speed, yaw rate, lateral acceleration, longitudinal acceleration, etc., to predict driver intent.”)
determine a lateral driving intent of the driver based on the driving information; (Abrash paragraph 0040 discloses, “The vehicle's ADAS/autonomous driving system may track and learn driving patterns for the host vehicle, including historical steering angle data, steering speed, vehicle speed, yaw rate, lateral acceleration, longitudinal acceleration, etc., to predict driver intent.”)
generate a velocity profile of the vehicle based on the longitudinal driving intent and the lateral driving intent; (Abrash paragraph 0014 discloses, “As yet another option, the target dynamics information may include location data, distance data, speed data, trajectory data, etc. of the target vehicle. In this instance, the electronic controller may communicate with the sensor array to receive lane line data for the lane of the road segment. In this instance, estimating the predicted lane assignment for the target vehicle may include comparing the target dynamics information and the lane line data.”) and (Hong paragraph 0039 teaches, “In another aspect, the intent prediction module 233 includes a model for each different maneuver classifiable by movement classifier 232. Based on the received selected maneuver, intent prediction module 233 can identify the corresponding appropriate model.”)
generate a curvature profile of the vehicle based on the longitudinal driving intent and the lateral driving intent; (Abrash paragraph 0038 discloses, “The host reference frame may be a vehicle-calibrated spatial frame of reference with a coordinate system that serves to describe the position of objects relative to the host and whose origin, orientation, and scale may be specified by a set of reference points. The received map data is aligned with respect to the host reference frame, e.g., locating the host vehicle between lane boundary lines and, if appropriate, placing a host reference frame origin on or adjacent a lane centerline. Once in the host vehicle reference frame, lane polynomial information such as width, host vehicle offset, heading, curvature, etc., may be derived for each of the available lanes on a road segment across which the host vehicle travels.”) and
determine a predicted path of the vehicle based on the velocity profile and the curvature profile. (Hong paragraph 0019 teaches, “At the host mobile robot, sensors collect sensor data used to identify and track surrounding robots/vehicles. Motion analysis algorithms can use the sensor data to predict if another robot/vehicle is likely to move laterally (e.g., between lanes) into the path of the host mobile robot, for example, when the other robot/vehicle is navigating a curve or is zigzagging. Motion analysis algorithms can also use sensor data to predict if another robot/vehicle is likely to move longitudinally (e.g., in the same lane) into the path of the host mobile robot, for example, when the other robot/vehicle is accelerating/decelerating rapidly or tailgating.”)
Abrash discloses an automated driving system and control logic for lane localization of target objects in mapped environments. Abrash does not disclose creating velocity profiles for observed target vehicles. Hong teaches of creating velocity profiles for observed target vehicles. Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to incorporate the teachings of Hong et.al. into the invention of Abrash. Such incorporation is motivated by the need to ensure collision doesn’t occur.
As per claim 15, A vehicle comprising the apparatus of claim 14. (Abrash paragraph 0006 discloses, “Presented herein are target acquisition systems with attendant control logic for target object detection and tracking, methods for making and methods for operating such systems, and intelligent motor vehicles with real-time lane localization for target objects in mapped environments.”)
As per claim 16, An autonomous vehicle comprising the apparatus of claim 14. (Abrash paragraph 0005 discloses, “Automated and autonomous vehicle systems may employ an assortment of commercially available components to provide target object detection and ranging.”)
As per claim 17, A non-transitory computer readable medium containing program instructions executed by a processor, the computer readable medium comprising:
program instructions that acquire driving information of a vehicle, wherein the driving information comprises at least one of a velocity, an acceleration, a steering angle, a steering angle velocity, a heading angle, a yaw rate, a stepped amount of an accelerator pedal and a brake pedal, or a gear shift setting of the vehicle; (Abrash paragraph 0040 discloses, “The vehicle's ADAS/autonomous driving system may track and learn driving patterns for the host vehicle, including historical steering angle data, steering speed, vehicle speed, yaw rate, lateral acceleration, longitudinal acceleration, etc., to predict driver intent.”)
program instructions that determine a longitudinal driving intent of a driver based on the driving information; (Abrash paragraph 0040 discloses, “The vehicle's ADAS/autonomous driving system may track and learn driving patterns for the host vehicle, including historical steering angle data, steering speed, vehicle speed, yaw rate, lateral acceleration, longitudinal acceleration, etc., to predict driver intent.”)
program instructions that determine a lateral driving intent of the driver based on the driving information; (Abrash paragraph 0040 discloses, “The vehicle's ADAS/autonomous driving system may track and learn driving patterns for the host vehicle, including historical steering angle data, steering speed, vehicle speed, yaw rate, lateral acceleration, longitudinal acceleration, etc., to predict driver intent.”)
program instructions that generate a velocity profile of the vehicle based on the longitudinal driving intent and the lateral driving intent; (Abrash paragraph 0014 discloses, “As yet another option, the target dynamics information may include location data, distance data, speed data, trajectory data, etc. of the target vehicle. In this instance, the electronic controller may communicate with the sensor array to receive lane line data for the lane of the road segment. In this instance, estimating the predicted lane assignment for the target vehicle may include comparing the target dynamics information and the lane line data.”) and (Hong paragraph 0039 teaches, “In another aspect, the intent prediction module 233 includes a model for each different maneuver classifiable by movement classifier 232. Based on the received selected maneuver, intent prediction module 233 can identify the corresponding appropriate model.”)
program instructions that generate a curvature profile of the vehicle based on the longitudinal driving intent and the lateral driving intent; (Abrash paragraph 0038 discloses, “The host reference frame may be a vehicle-calibrated spatial frame of reference with a coordinate system that serves to describe the position of objects relative to the host and whose origin, orientation, and scale may be specified by a set of reference points. The received map data is aligned with respect to the host reference frame, e.g., locating the host vehicle between lane boundary lines and, if appropriate, placing a host reference frame origin on or adjacent a lane centerline. Once in the host vehicle reference frame, lane polynomial information such as width, host vehicle offset, heading, curvature, etc., may be derived for each of the available lanes on a road segment across which the host vehicle travels.”) and
program instructions that determine a predicted path of the vehicle based on the velocity profile and the curvature profile. (Hong paragraph 0019 teaches, “At the host mobile robot, sensors collect sensor data used to identify and track surrounding robots/vehicles. Motion analysis algorithms can use the sensor data to predict if another robot/vehicle is likely to move laterally (e.g., between lanes) into the path of the host mobile robot, for example, when the other robot/vehicle is navigating a curve or is zigzagging. Motion analysis algorithms can also use sensor data to predict if another robot/vehicle is likely to move longitudinally (e.g., in the same lane) into the path of the host mobile robot, for example, when the other robot/vehicle is accelerating/decelerating rapidly or tailgating.”)
Abrash discloses an automated driving system and control logic for lane localization of target objects in mapped environments. Abrash does not disclose creating velocity profiles for observed target vehicles. Hong teaches of creating velocity profiles for observed target vehicles. Therefore, at the time of filing, it would have been obvious to one of ordinary skill in the art to incorporate the teachings of Hong et.al. into the invention of Abrash. Such incorporation is motivated by the need to ensure collision doesn’t occur.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TYLER D PAIGE whose telephone number is (571)270-5425. The examiner can normally be reached M-F 7:00am - 6:00pm (mst).
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/TYLER D PAIGE/Primary Examiner, Art Unit 3664
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