DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This Office Action is in response to the application filed on February 16, 2024. Claims 1-21 were previously amended. Claims 1-21 are presently pending and are presented for examination.
Claims Objections
Claim 13 is objected to because of the following informalities:
Claim 13 recites “wherein the second vehicle model a static vehicle model” and should recites “wherein the second vehicle model is a static vehicle model” or something similar.
Appropriate correction is required.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to ATA 35 U.S.C. 102 and 103 is incorrect, any correction of the statutory basis 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 set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
Determining the scope and contents of the prior art.
Ascertaining the differences between the prior art and the claims at issue.
Resolving the level of ordinary skill in the pertinent art.
Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1, 3, 6-7, 9-12, and 20-21 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 2020/0142405 (hereinafter, "Havens") in view of U.S. Pub. No. 2023/0035526 (hereinafter, "Okuda").
Regarding claim 1, Havens discloses a control system for autonomous drive of a heavy-duty vehicle, comprising
an autonomous drive (“AD”) controller configured with a trajectory tracking function arranged to track a desired vehicle trajectory (“the autonomous control unit may be configured to incorporate data from the image processing module 200, the GPS transceiver, the RADAR, the LIDAR, the cameras, and other vehicle subsystems to determine the driving path or trajectory for the vehicle” (para 0031)), and a first vehicle model arranged to model a response by the vehicle to a requested steering angle (“a system and method for modeling the response of an autonomous vehicle to potential control commands (e.g., a steering wheel position command, acceleration and/or brake pedal control commands, etc.)” (para 0042)), where the AD controller is arranged to generate a steering angle request based on the desired vehicle trajectory and on the first vehicle model (“Control of the semi-truck 200 may include generating commands such as steering, throttle (e.g., acceleration), and/or brake commands in order to match the movement of the semi-truck 200 to the desired trajectory 215. In some embodiments, the determination of values for the control commands may involve modeling the physical response of the semi-truck 200 to potential commands in order to predict values for the commands which will result in the semi-truck 200 following the desired trajectory 215 with minimal error” (para 0043)),
the control system further comprising a vehicle motion management (“VMM”) function (“The vehicle operational subsystem 300 may include a model predictive controller (MPC) 330 (as illustrated in FIG. 3). A model of the complex systems used to describe the motion of the semi-truck 200 in response to the input commands” (para 0046)) arranged to receive the steering angle request from the AD controller (“the adaptive controller 340 is configured to receive the command 505 from the QP solver 335” (para 0068)), … , where the VMM function is arranged to obtain a desired vehicle behavior based on a response of the second vehicle model to the steering angle request (“receiving a desired trajectory and a vehicle status of a semi-truck, determining a dynamic model of the semi-truck based on the desired trajectory and the vehicle status, generating at least one control command for controlling the semi-truck based on the dynamic model and at least one quadratic program (QP) problem, and outputting the at least one control command” (para 0006)), where the VMM function is arranged to determine motion support device control set-points in dependence of the desired vehicle behavior (“receiving a desired trajectory and a vehicle status of a semi-truck, determining a dynamic model of the semi-truck based on the desired trajectory and the vehicle status, generating at least one control command for controlling the semi-truck based on the dynamic model and at least one quadratic program (QP) problem, and outputting the at least one control command” (para 0006)).
However, Havens does not explicitly teach
… where the VMM function comprises a second vehicle model configured to be aligned with the first vehicle model.
Okuda, in the same field of endeavor, teaches
… where the VMM function comprises a second vehicle model configured to be aligned with the first vehicle model (“the inference device 1 uses a second machine learning model 17 to determine whether or not the driving assistance information inferred by the first machine learning model 16 is valid. The second machine learning model 17 has learned, by using the same input data as the first machine learning model 16, so that input data and output data that is an inference result (hereinafter referred to as “second inference result”) are equal. In the first embodiment, the second machine learning model 17 has learned to use the vehicle peripheral data as an input, and to output output data having the same content as the vehicle peripheral data (hereinafter referred to as “output vehicle peripheral data”) as the second inference result” (para 0030)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Havens with the teachings of Okuda in order to determine whether or not the driving assistance information inferred by the first machine learning model is valid; see Okuda at least at [0030].
Regarding claim 3, Havens discloses and Okuda teaches the control system according to claim 1. Additionally, Havens discloses wherein the trajectory tracking function is arranged to determine the steering angle request based on the first vehicle model (“the adaptive controller 340 and the PID controller 345 may respectively determine the output steering wheel command 350 and the output pedal command 355 to drive the semi-truck 200 to the desired set points” (para 0060) and “generate at least one control command for controlling the semi-truck based on the updated dynamic model and at least one quadratic program (QP) problem, output the at least one control command” (para 0003)).
Regarding claim 6, Havens discloses and Olson teaches the control system according to claim 1. Additionally, Havens discloses wherein the first vehicle model is configured to output an expected state of a towing vehicle unit of the heavy-duty vehicle in dependence of an applied steering wheel angle (“modeling the response of an autonomous vehicle to potential control commands (e.g., a steering wheel position command, acceleration and/or brake pedal control commands, etc.) to predict the vehicle's response to the control commands” (para 0042)), where the expected state comprises any of a lateral position of the towing unit , a lateral deviation from a desired trajectory of a point of the towing unit (“defining the various error(s) between the current state of the semi-truck 200 and the desired trajectory 215. In the illustrated example, the error(s) may include a heading error eψ and a relative lateral position error ey” (para 0044)), a yaw rate (“the vehicle status may include the position, velocity, yaw angle, and/or yaw angle rate” (para 0057)), a lateral acceleration, a lateral velocity (“The dynamic linear time-invariant model 331 may include a lateral dynamics model 332 and a longitudinal kinematic model 333” (para 0055)), a longitudinal position, a longitudinal acceleration, and a longitudinal velocity of the towing vehicle unit (“the output steering wheel command 350 and the output pedal command 355 may be respectively provided to the steering wheel and acceleration and/or brake pedal(s) or actuator(s) controlling the steering position and acceleration/deceleration of the semi-truck 200” (para 0053)).
Regarding claim 7, Havens discloses and Olson teaches the control system according to claim 1. Additionally, Havens discloses wherein the first vehicle model is configured to output an expected state of a towed vehicle unit of the heavy-duty vehicle in dependence of an applied steering wheel angle (“a system and method for modeling the response of an autonomous vehicle to potential control commands (e.g., a steering wheel position command, acceleration and/or brake pedal control commands, etc.); The semi-truck 200 can include a tractor 205 and a trailer 210, which may be connected at a pivot point, for example, via a fifth-wheel hitch; this disclosure can also be adapted for the autonomous control of other types of vehicles 105, including passenger vehicles, articulated buses, tractors 205 pulling multiple trailers 210, etc. ”(para 0042) and “The lateral dynamics model 332 may also be configured to receive internal dynamic parameters of the semi-truck 200, which may include one or more of the mass of the semi-truck 200, moment of inertia of the semi-truck 200, trailer angle ψ.sub.f, cornering stiffness, articulation between the tractor 205 and trailer 210, etc” (para 0055)), where the expected state comprises any of an articulation angle of the towed unit, a lateral position of the towed unit, a lateral deviation from a desired trajectory of a point of the towed unit, a yaw rate, a lateral acceleration, a lateral velocity, a longitudinal position, a longitudinal acceleration, and a longitudinal velocity of the towed vehicle unit (“The lateral dynamics model 332 may also be configured to receive internal dynamic parameters of the semi-truck 200, which may include one or more of the mass of the semi-truck 200, moment of inertia of the semi-truck 200, trailer angle ψ.sub.f, cornering stiffness, articulation between the tractor 205 and trailer 210, etc” (para 0055)).
Regarding claim 9, Havens discloses and Okuda teaches the control system according to claim 1. Additionally, Havens discloses wherein the desired vehicle behavior is represented by any of a lateral position of a point on a vehicle unit of the heavy-duty vehicle, a yaw rate of a vehicle unit, a lateral acceleration of a vehicle unit, a lateral velocity of a vehicle unit, and a body sideslip of a vehicle unit (“the determination of values for the control commands may involve modeling the physical response of the semi-truck 200 to potential commands in order to predict values for the commands which will result in the semi-truck 200 following the desired trajectory 215 with minimal error” (para 0043) and (“the processor updates the dynamic linear time-invariant model 331 of the semi-truck 200 based on the desired trajectory 215 and the vehicle status. At block 415, the processor determines a lateral control QP problem and a longitudinal control QP problem based on the dynamic linear time-invariant model 331” (para 0064)).
Regarding claim 10, Havens discloses and Okuda teaches the control system according to claim 1. However, Havens does not explicitly teach wherein the second vehicle model is configured to be aligned with the first vehicle model by exchange of a pre-determined number of model parameter values.
Okuda, in the same field of endeavor, teaches
wherein the second vehicle model is configured to be aligned with the first vehicle model by exchange of a pre-determined number of model parameter values (“the inference device 1 uses a second machine learning model 17 to determine whether or not the driving assistance information inferred by the first machine learning model 16 is valid. The second machine learning model 17 has learned, by using the same input data as the first machine learning model 16, so that input data and output data that is an inference result (hereinafter referred to as “second inference result”) are equal. In the first embodiment, the second machine learning model 17 has learned to use the vehicle peripheral data as an input, and to output output data having the same content as the vehicle peripheral data (hereinafter referred to as “output vehicle peripheral data”) as the second inference result” (para 0030) and “The data acquisition unit 11 acquires vehicle peripheral data to be input to the first machine learning model 16 and the second machine learning model 17” (para 0046)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Havens with the teachings of Okuda in order to determine whether or not the driving assistance information inferred by the first machine learning model is valid; see Okuda at least at [0030].
Regarding claim 11, Havens discloses and Okuda teaches the control system according to claim 10. Additionally, Havens discloses wherein the model parameter values correspond to any of a vehicle unit mass (“The dynamic model may represent the internal dynamics of the semi-truck 200, including the mass” (para 0049)), a vehicle unit yaw inertia, a tire stiffness (“The dynamic model may represent the internal dynamics of the semi-truck 200, including the mass, moment of inertia, trailer angle ψ.sub.f, and the cornering stiffness at each tire of the semi-truck” (para 0049)), a road friction level (“the dynamic model may be based on Lagrangian mechanics, which describes the semi-truck's 200 response to certain input forces (which in turn may be based on the commands and/or external forces such as the slope of the road, curvature of a corner, etc.)” (para 0049)), a vehicle longitudinal velocity (“the kinematic model of the semi-truck 200 may take into account the current velocity and position of the semi-truck 200 as a whole and predict the future movement of the semi-truck 200 in response to the input commands provided to the vehicle drive subsystem” (para 0048)), an axle load (“different trailer 210 loads may result in different control commands due to the dynamic modelling of those trailer loads” (para 0067)), a vehicle unit wheelbase, a vehicle unit coupling position, and a vehicle type (“a semi-truck 200 as an example vehicle which can be controlled using dynamic modelling, this disclosure can also be adapted for the autonomous control of other types of vehicles 105, including passenger vehicles, articulated buses, tractors 205 pulling multiple trailers 210, etc” (para 0042)).
Regarding claim 12, Havens discloses and Okuda teaches the control system according to claim 1. However, Havens does not explicitly teach wherein the second vehicle model is configured to be aligned with the first vehicle model by having one or more corresponding or identical states.
Okuda, in the same field of endeavor, teaches
wherein the second vehicle model is configured to be aligned with the first vehicle model by having one or more corresponding or identical states (“the inference device 1 uses a second machine learning model 17 to determine whether or not the driving assistance information inferred by the first machine learning model 16 is valid. The second machine learning model 17 has learned, by using the same input data as the first machine learning model 16, so that input data and output data that is an inference result (hereinafter referred to as “second inference result”) are equal. In the first embodiment, the second machine learning model 17 has learned to use the vehicle peripheral data as an input, and to output output data having the same content as the vehicle peripheral data (hereinafter referred to as “output vehicle peripheral data”) as the second inference result” (para 0030)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Havens with the teachings of Okuda in order to determine whether or not the driving assistance information inferred by the first machine learning model is valid; see Okuda at least at [0030].
Regarding claim 20, Havens discloses a control unit for vehicle motion management (“VMM”) of a heavy-duty vehicle (Fig. 3, #345), wherein the control unit is arranged to implement a vehicle motion management VMM function (“The vehicle operational subsystem 300 may include a model predictive controller (MPC) 330 (as illustrated in FIG. 3). A model of the complex systems used to describe the motion of the semi-truck 200 in response to the input commands” (para 0046)), the control unit comprising processing circuitry configured to receive a steering angle request from an AD controller (“the adaptive controller 340 is configured to receive the command 505 from the QP solver 335” (para 0068)), ... , where the VMM function is arranged to obtain a desired vehicle behavior based on a response of the second vehicle model to the steering angle request (“receive a desired trajectory and a vehicle status of the semi-truck” (para 0003) and “receiving a desired trajectory and a vehicle status of a semi-truck, determining a dynamic model of the semi-truck based on the desired trajectory and the vehicle status, generating at least one control command for controlling the semi-truck based on the dynamic model and at least one quadratic program (QP) problem, and outputting the at least one control command” (para 0006)), where the VMM function is arranged to determine motion support device control set points in dependence of the desired vehicle behavior (“receiving a desired trajectory and a vehicle status of a semi-truck, determining a dynamic model of the semi-truck based on the desired trajectory and the vehicle status, generating at least one control command for controlling the semi-truck based on the dynamic model and at least one quadratic program (QP) problem, and outputting the at least one control command” (para 0006)).
However, Havens does not explicitly teach
... where the VMM function comprises a second vehicle model configured to be aligned with a first vehicle model of the AD controller.
Okuda, in the same field of endeavor, teaches
... where the VMM function comprises a second vehicle model configured to be aligned with a first vehicle model of the AD controller (“the inference device 1 uses a second machine learning model 17 to determine whether or not the driving assistance information inferred by the first machine learning model 16 is valid. The second machine learning model 17 has learned, by using the same input data as the first machine learning model 16, so that input data and output data that is an inference result (hereinafter referred to as “second inference result”) are equal. In the first embodiment, the second machine learning model 17 has learned to use the vehicle peripheral data as an input, and to output output data having the same content as the vehicle peripheral data (hereinafter referred to as “output vehicle peripheral data”) as the second inference result” (para 0030)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Havens with the teachings of Okuda in order to determine whether or not the driving assistance information inferred by the first machine learning model is valid; see Okuda at least at [0030].
Regarding claim 21, Havens discloses a computer-implemented method performed in a vehicle control unit for autonomously controlling motion of a heavy-duty vehicle (“a system and method for dynamic predictive control of autonomous vehicles” (para 0013)), the method comprising configuring a trajectory tracking function in an autonomous drive (“AD”) controller, wherein the trajectory tracking function is arranged to track a desired vehicle trajectory (“the autonomous control unit may be configured to incorporate data from the image processing module 200, the GPS transceiver, the RADAR, the LIDAR, the cameras, and other vehicle subsystems to determine the driving path or trajectory for the vehicle” (para 0031)),
configuring a first vehicle model arranged to model a response by the vehicle to a requested steering angle (“a system and method for modeling the response of an autonomous vehicle to potential control commands (e.g., a steering wheel position command, acceleration and/or brake pedal control commands, etc.)” (para 0042)),
generating a steering angle request by the AD controller based on the desired vehicle trajectory and on the first vehicle model (“Control of the semi-truck 200 may include generating commands such as steering, throttle (e.g., acceleration), and/or brake commands in order to match the movement of the semi-truck 200 to the desired trajectory 215. In some embodiments, the determination of values for the control commands may involve modeling the physical response of the semi-truck 200 to potential commands in order to predict values for the commands which will result in the semi-truck 200 following the desired trajectory 215 with minimal error” (para 0043)), and
configuring a vehicle motion management (“VMM”) function (“The vehicle operational subsystem 300 may include a model predictive controller (MPC) 330 (as illustrated in FIG. 3). A model of the complex systems used to describe the motion of the semi-truck 200 in response to the input commands” (para 0046)) to receive the steering angle request from the AD controller (“the adaptive controller 340 is configured to receive the command 505 from the QP solver 335” (para 0068))... , where the VMM function is arranged to obtain a desired vehicle behavior based on a response of the second vehicle model to the steering angle request (“receive a desired trajectory and a vehicle status of the semi-truck” (para 0003) and “receiving a desired trajectory and a vehicle status of a semi-truck, determining a dynamic model of the semi-truck based on the desired trajectory and the vehicle status, generating at least one control command for controlling the semi-truck based on the dynamic model and at least one quadratic program (QP) problem, and outputting the at least one control command” (para 0006)), where the VMM function is arranged to determine motion support device control set points in dependence of the desired vehicle behavior (“receiving a desired trajectory and a vehicle status of a semi-truck, determining a dynamic model of the semi-truck based on the desired trajectory and the vehicle status, generating at least one control command for controlling the semi-truck based on the dynamic model and at least one quadratic program (QP) problem, and outputting the at least one control command” (para 0006)).
However, Havens does not explicitly teach
… where the VMM function comprises a second vehicle model configured to be aligned with the first vehicle model.
Okuda, in the same field of endeavor, teaches
… where the VMM function comprises a second vehicle model configured to be aligned with the first vehicle model (“the inference device 1 uses a second machine learning model 17 to determine whether or not the driving assistance information inferred by the first machine learning model 16 is valid. The second machine learning model 17 has learned, by using the same input data as the first machine learning model 16, so that input data and output data that is an inference result (hereinafter referred to as “second inference result”) are equal. In the first embodiment, the second machine learning model 17 has learned to use the vehicle peripheral data as an input, and to output output data having the same content as the vehicle peripheral data (hereinafter referred to as “output vehicle peripheral data”) as the second inference result” (para 0030)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Havens with the teachings of Okuda in order to determine whether or not the driving assistance information inferred by the first machine learning model is valid; see Okuda at least at [0030].
Claims 2, 5, and 13 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 2020/0142405 (hereinafter, "Havens") in view of U.S. Pub. No. 2023/0035526 (hereinafter, "Okuda") as applied to claim 1 above, and in further view of U.S. Pub. No. 2021/0291862 (hereinafter, "Jiang").
Regarding claim 2, Havens discloses and Okuda teaches the control system according to claim 1. However, Havens does not explicitly teach wherein the trajectory tracking function is arranged to track the desired vehicle trajectory by optimization of a cost function which is at least partly based on an output of the first vehicle model.
Jiang, in the same field of endeavor, teaches
wherein the trajectory tracking function is arranged to track the desired vehicle trajectory by optimization of a cost function which is at least partly based on an output of the first vehicle model (“The optimizer can use a cost function and the vehicle model to generate a sequence of control commands (e.g., throttle, steering, and/or brake commands) that track the vehicle's path along a target vehicle trajectory” (para 0044)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Havens with the teachings of Jiang in order to penalize undesirable behavior; see Jiang at least at [0044].
Regarding claim 5, Havens discloses and Okuda teaches the control system according to claim 1. However, Havens does not explicitly teach wherein the first vehicle model is a static vehicle model, such as a yaw gain model of the heavy-duty vehicle or a dynamic vehicle model, such as a linear one-track model of the heavy-duty vehicle.
Jiang, in the same field of endeavor, teaches
wherein the first vehicle model is a static vehicle model, such as a yaw gain model of the heavy-duty vehicle (“An MPC (model predictive control) may use static optimization algorithms and a static vehicle model to generate the optimized sequence of commands” (para 0005)) or a dynamic vehicle model, such as a linear one-track model of the heavy-duty vehicle (“Operation 502 includes applying the control command to a dynamic model of the ADV to simulate behavior of the ADV” (para 0054)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Havens with the teachings of Jiang in order to model behavior of the ADV more accurately by accounting for more nuanced and non-linear behavior of the ADV; see Jiang at least at [0054].
Regarding claim 13, Havens discloses and Okuda teaches the control system according to claim 1. However, Havens does not explicitly teach wherein the second vehicle model a static vehicle model, such as a yaw gain model or a dynamic vehicle model.
Jiang, in the same field of endeavor, teaches
wherein the second vehicle model a static vehicle model, such as a yaw gain model or a dynamic vehicle model (“An MPC (model predictive control) may use static optimization algorithms and a static vehicle model to generate the optimized sequence of commands” (para 0005)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Havens with the teachings of Jiang in order to generate the optimized sequence of commands; see Jiang at least at [0005].
Claims 4, 8 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 2020/0142405 (hereinafter, "Havens") in view of U.S. Pub. No. 2023/0035526 (hereinafter, "Okuda") as applied to claim 1 above, and in further view of U.S. Pub. No. 2021/0403081 (hereinafter, "Funke").
Regarding claim 4, Havens discloses and Okuda teaches the control system according to claim 1. However, Havens does not explicitly teach wherein the steering angle request corresponds to one or more virtual road wheel angles.
Funke, in the same field of endeavor, teaches
wherein the steering angle request corresponds to one or more virtual road wheel angles (“The kinematic vehicle model may assume zero lateral velocity, that is, no sideslip for the vehicle, at a point representing the vehicle. The point may be equidistant from a first axle through the leading wheels of the vehicle and a second axle through the trailing wheels of the vehicle. Using this kinematic vehicle model, the planning component may determine a first steering angle for leading wheels of the vehicle to navigate to a destination, e.g., based on a current state of the vehicle. In examples, the planning component can determine a series of vehicle states, including leading wheel steering angles, as a trajectory. The kinematic vehicle model assumes zero lateral velocity, and thus implicitly that the trailing steering angle is the additive inverse of the leading steering angle. Stated differently, the kinematic vehicle model assumes mirrored steering for the vehicle, e.g., about a lateral axis through the point representing the vehicle” (para 0011)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Havens with the teachings of Funke in order to follow a trajectory using four-wheel steering; see Funke at least at [0006].
Regarding claim 8, Havens discloses and Okuda teaches the control system according to claim 1. However, Havens does not explicitly teach wherein the steering angle request is a vector of steering angles for one or more steered virtual axles.
Funke, in the same field of endeavor, teaches
wherein the steering angle request is a vector of steering angles for one or more steered virtual axles (“determining four-wheel steering commands, e.g., the leading wheel commands 132 and the trailing wheel commands 134, to correct for steering-related or tracking errors, such as heading, lateral offset, or the like” (para 0030)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Havens with the teachings of Funke in order to correct for steering-related or tracking errors, such as heading, lateral offset, or the like; see Funke at least at [0030].
Regarding claim 14, Havens discloses and Okuda teaches the control system according to claim 1. However, Havens does not explicitly teach wherein the VMM function is configured to determine a steering offset value, and to determine the motion support device control set points in dependence of the steering offset value.
Funke, in the same field of endeavor, teaches
wherein the VMM function is configured to determine a steering offset value, and to determine the motion support device control set points in dependence of the steering offset value (“The process 500 may also include integral controlling, which allows to vehicle to reject constant steering offsets as well as allow for steady-state lateral errors during a ramp steer. When the steering angles remain below their limits, the integral steering angles integrate both lateral error and the sum of heading error and steady-state sideslip” (para 0062) and “determining a lateral offset of the autonomous vehicle relative to the path, and determining an angular offset between a current heading of the vehicle and the trajectory; and the determining the first corrective steering angle and the second corrective steering angle comprises minimizing the lateral offset and the angular offset” (para 0111)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Havens with the teachings of Funke in order to calculate deviations from target states; see Funke at least at [0030].
Claims 15 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 2020/0142405 (hereinafter, "Havens") in view of U.S. Pub. No. 2023/0035526 (hereinafter, "Okuda") as applied to claim 1 above, and in further view of U.S. Pub. No. 2021/0331663 (hereinafter, "Newton").
Regarding claim 15, Havens discloses and Okuda teaches the control system according to claim 1. However, Havens does not explicitly teach a third vehicle model configured for determining the motion support device control allocation set points based on the desired vehicle behavior.
Newton, in the same field of endeavor, teaches
a third vehicle model configured for determining the motion support device control allocation set points based on the desired vehicle behavior (“a trained ML model that maps the above inputs to an expected vehicle performance” (para 0046) and “apply a simple Ackermann steering model with simple torque mapping to perform an Automotive Safety integrity Level (ASIL) D analysis and generate safe wheel torque control and steering angle control parameters that are passed onto the safety unit” (para 0055)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Havens with the teachings of Newton in order to provide control signals to and receive information from an electric drivetrain system (EDS) 150 of the vehicle; see Newton at least at [0020].
Regarding claim 16, Havens discloses and Okuda teaches the control system according to claim 15. However, Havens does not explicitly teach wherein the third vehicle model comprises any of an estimate of vehicle motion caused by side wind, an estimate of vehicle motion caused by road banking, an estimate of vehicle motion caused by axle misalignment, an estimate of vehicle motion caused by a difference in tire radius, a model of compliance in a steering system of the heavy-duty vehicle, and a model of suspension travel effect on a steering system of the heavy-duty vehicle.
Newton, in the same field of endeavor, teaches
wherein the third vehicle model comprises any of an estimate of vehicle motion caused by side wind, an estimate of vehicle motion caused by road banking, an estimate of vehicle motion caused by axle misalignment, an estimate of vehicle motion caused by a difference in tire radius, a model of compliance in a steering system of the heavy-duty vehicle, and a model of suspension travel effect on a steering system of the heavy-duty vehicle (“The predictive conditioner 154 is trained to control a simulated vehicle (“agent vehicle”) to mimic the advisor's behavior in a realistically simulated, high-fidelity environment, which includes varying friction values, slopes and banks on the road, potholes, and other external forces (e.g. lateral forces caused by wind blasts)” (para 0062) and “modify parameters for an expected driver experience, such as parameters regarding suspension system performance and acceleration rates, among other things” (para 0036)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Havens with the teachings of Newton in order to provide control signals to and receive information from an electric drivetrain system (EDS) 150 of the vehicle; see Newton at least at [0020].
Claims 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Pub. No. 2020/0142405 (hereinafter, "Havens") in view of U.S. Pub. No. 2023/0035526 (hereinafter, "Okuda") as applied to claim 1 above, and in further view of U.S. Pat. No. 9,315,178 (hereinafter, "Ferguson").
Regarding claim 17, Havens discloses and Okuda teaches the control system according to claim 1. However, Havens does not explicitly teach compare a current vehicle state of the heavy-duty vehicle to a threshold limit set in dependence of the desired vehicle behavior, and to trigger an action if the threshold is breached.
Ferguson, in the same field of endeavor, teaches
compare a current vehicle state of the heavy-duty vehicle to a threshold limit set in dependence of the desired vehicle behavior, and to trigger an action if the threshold is breached (“calculating a predicted output value based on the at least one indication of the input and a state of the vehicle, comparing the predicted output value with the indication of the output, if the comparison is not within a threshold range, responsively creating an alert indicator, and activating an alert action in response to the creation of the alert indicator” (Col. 2, lines 53-59)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Havens with the teachings of Ferguson in order to activate an alert action responsive to the alert; see Ferguson at least at [Col. 2, lines 5-6].
Regarding claim 18, Havens discloses and Okuda teaches the control system according to claim 17. However, Havens does not explicitly teach wherein the control system is arranged to brake the vehicle if the threshold limit is passed, by modification of the motion support device control allocation set points.
Ferguson, in the same field of endeavor, teaches
wherein the control system is arranged to brake the vehicle if the threshold limit is passed, by modification of the motion support device control allocation set points (“In response to an alert indicator being created, an alert action could be activated by the computer system located on-board the vehicle. Alert actions could include, but should not be limited to, changing a driving mode of the vehicle to a safe driving mode, slowing the vehicle to a stop, pulling the vehicle over to a side of a road, releasing control of the vehicle back to a driver. Other alert actions are possible” (Col. 3, lines 57-64)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Havens with the teachings of Ferguson in order to activate an alert action responsive to the alert; see Ferguson at least at [Col. 2, lines 5-6].
Regarding claim 19, Havens discloses and Okuda teaches the control system according to claim 17. However, Havens does not explicitly teach wherein the action comprises generation of a fault condition signal sent to the AD controller.
Ferguson, in the same field of endeavor, teaches
wherein the action comprises generation of a fault condition signal sent to the AD controller (“In response to an alert indicator being created, an alert action could be activated by the computer system located on-board the vehicle. Alert actions could include, but should not be limited to, changing a driving mode of the vehicle to a safe driving mode, slowing the vehicle to a stop, pulling the vehicle over to a side of a road, releasing control of the vehicle back to a driver. Other alert actions are possible” (Col. 3, lines 57-64)).
One of ordinary skill in the art, before the time of filing, would have been motivated to modify the disclosure of Havens with the teachings of Ferguson in order to activate an alert action responsive to the alert; see Ferguson at least at [Col. 2, lines 5-6].
Conclusion
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/ADAM M ALHARBI/Primary Examiner, Art Unit 3663