DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments/Amendments
The amendment filed September 3rd, 2025 has been entered. Claims 1-22 are currently pending in the Application.
Applicant’s amendments with respect to the rejection of claims under 35 U.S.C 103 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-7, 9-16 and 18-22, is/are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Patent Publication No. 2021/0110484 Shalev-Shwartz et al (hereinafter Shalev-Shwartz), and further in view of U.S. Patent No. 11932306 , to Febbo et al (hereinafter Febbo), and further in view of U.S. Patent Publication No. 20120330542, to Hafner et al (hereinafter Hafner)
Regarding claim 1, and commensurate claim 19, Shalev-Shwartz discloses, A trajectory planning system for an autonomous vehicle, the trajectory planning system comprising: ([0005] “systems and methods for autonomous vehicle navigation”)
one or more controllers in electronic communication with one or more external vehicle networks that collect data with respect to one or more moving obstacles located in an environment surrounding the autonomous vehicle ([0089] “Processing unit 110 may comprise various types of devices. For example, processing unit 110 may include various devices, such as a controller, an image preprocessor, a central processing unit (CPU), support circuits, digital signal processors, integrated circuits, memory, or any other types of devices for image processing and analysis.”)
the one or more controllers executing instructions to: determine a discrete-time relative vehicle state based on an autonomous vehicle dynamics model ([0197] [0449] “Working in discrete time intervals, at time t”)
determine, based on the discrete-time relative vehicle state, a position avoidance set representing relative lateral positions and longitudinal positions that the autonomous vehicle avoids while bypassing the one or more moving obstacles when performing a maneuver ([0197] “Working in discrete time intervals, at time t, the current state S.sub.t?S may be observed, and the policy may be applied to obtain a desired action”). Further, ([0554] “lateral maneuver”).
determine a set of offline ego states for which the autonomous vehicle is unable to execute maneuvers without entering the position avoidance set ([0368]-[0369] “navigation based on accident liability constraint As described in the sections above, planned navigational actions may be tested against predetermined constraints to ensure compliance with certain rules. In some embodiments, this concept may be extended to considerations of potential accident liability. As discussed below, a primary goal of autonomous navigation is safety. As absolute safety may be impossible (e.g., at least because a particular host vehicle under autonomous control cannot control the other vehicles in its surroundings—it can only control its own actions), the use of potential accident liability as a consideration in autonomous navigation and, indeed, as a constraint to planned actions may help ensure that a particular autonomous vehicle does not take any actions that are deemed unsafe”). Further, ([0370] “host vehicle 1901 may be unable to avoid an accident with at least one of the target vehicles should vehicle 1905, for example, suddenly cut in to the host vehicle's lane on a collision course with the host vehicle. To address this difficulty, a typical response of autonomous vehicle practitioners is to resort to a statistical data-driven approach”);
approximate, in real-time, a set of real-time ego states of the autonomous vehicle by a function approximator, wherein the function approximator has been trained during a supervised learning process with the set of offline ego states as a ground-truth dataset; ([0232] [0535] “For the training of ?.sub.t+1 given s.sub.t, a.sub.t, supervised learning may be used together with real data. For training the policy of nodes simulators can be used. Later, fine tuning of a policy can be accomplished using real data. Two concepts may make the simulation more realistic. First, using imitation, an initial policy can be constructed using the “behavior cloning” paradigm, using large real world data sets. In some cases, the resulting agents may be suitable. In other cases, the resulting agents at least form very good initial policies for the other agents on the roads”)
compute a plurality of relative state trajectories for the autonomous vehicle, wherein the plurality of relative state trajectories avoid intersecting the set of real-time ego states of autonomous vehicle; ([0273] [0370] “in order to avoid a collision with the detected stationary object. “);
and select a trajectory from the plurality of relative state trajectories for the autonomous vehicle, wherein the autonomous vehicle follows the trajectory while performing the maneuver. ([0613] “the system for navigating a host vehicle may include at least one processing device programmed to receive, from an image capture device, at least one image representative of an environment of the host vehicle (e.g., a visible image, LIDAR image, RADAR image, etc.); receive from at least one sensor an indicator of a current navigational state of the host vehicle; and determine, based on analysis of the at least one image and based on the indicator of the current navigational state of the host vehicle, that a collision between the host vehicle and one or more objects is unavoidable. The processor may evaluate available alternatives. For example, the processor may determine, based on at least one driving policy, a first planned navigational action for the host vehicle involving an expected collision with a first object and a second planned navigational action for the host vehicle involving an expected collision with a second object. The first and second planned navigational actions may be tested against at least one accident liability rule for determining potential accident liability. If the test of the first planned navigational action against the at least one accident liability rule indicates that potential accident liability may exist for the host vehicle if the first planned navigational action is taken, then the processor may cause the host vehicle not to implement the first planned navigational action. If the test of the second planned navigational action against the at least one accident liability rule indicates that no accident liability would result for the host vehicle if the second planned navigational action is taken, then the processor may cause the host vehicle to implement the second planned navigational action. The objects may include other vehicles or non-vehicle objects (e.g., road debris, trees, poles, signs, pedestrians, etc”).
Shalev-Shwartz does not explicitly disclose, however Febbo discloses, wherein the one or more controllers determine the plurality of relative state trajectories for the autonomous vehicle based on an initial state of the autonomous vehicle, a final state of the autonomous vehicle, and one or more levels of driving aggression; (See [Column 6 Line 26-48] “The trajectory planner 130 may be implemented via the processor 102 and may generate a trajectory for an autonomous vehicle based on the tracked obstacle, the goal or the human operator, and a non-linear model predictive control (NMPC). The NMPC may be modeled based on a constant initial time, a variable final time, a state, a number of states, a control, a number of controls, a desired initial state vector, an initial state tolerance vector, a desired final state vector, a final state tolerance vector, arrays of slack variables on the initial states and terminal states, and arrays of weights. As seen in FIG. 1, the trajectory planner 130 receives inputs from the set of sensors 110, the perception controller 120, vehicle encoders 140, and the localization controller 150 and generates the trajectory for the autonomous vehicle as a steering angle δ.sub.f and a velocity u.sub.x when provided with the goal and tracked obstacles from the perception controller 120 and a vehicle state ε.sub.0 from the localization controller 150. Together, the output from both the perception controller 120 and the localization controller 150 may establish the trajectory for the trajectory planner 130. As discussed above, the output of the trajectory planner 130 may be the desired steering angle δ.sub.f and speed or velocity u.sub.x, which may be fed to the low-level controller 160.”). Further (See [Column 9 Line 53-55] “a minimum final time term makes the planner calculate more aggressive trajectories,”).
Further Hafner discloses, wherein a region of interest exists between the one or more moving obstacles and the autonomous vehicle, (See at least paragraph [0020] “collision avoidance (ICA) system for preventing two or more vehicles from colliding at an intersection. The ICA system can calculate predicted positions of the two or more vehicles in the near future, and both the current and future positions can be broadcast to surrounding vehicles using vehicle-to-vehicle communication. For each vehicle, a set of states, for example position, speed, acceleration, and the like, where a collision is imminent can be identified using state information for a local vehicle, a remote vehicle, and a known collision zone for the intersection. If the current states of the vehicles are determined to be in danger of entering the collision zone, the ICA system can control the vehicles to perform evasive driving maneuvers and/or alert the drivers.”). Further, (See at least paragraph [0072] “detect upcoming collisions between at least two vehicles approaching an intersection ”).
and wherein the region of interest represents an area along a roadway (See at least paragraph [0035] “The ICA system incorporates a longitudinal displacement along a predefined path, for example a road lane, to represent a vehicle position.”).
where the autonomous vehicle is unable to execute a maneuver during a time horizon while avoiding contact with the one or more moving obstacles, (See at least paragraph [0039-0040] “The series of rectangles propagating back towards the origin of the graph represents back-propagation steps from the bad set as a function of time. The capture set is the union of the back-propagation rectangles and the bad set. As stated earlier, the capture set represents all system configurations from which at least two vehicles are guaranteed to enter the bad set B regardless of control action taken. For example, consider a vehicle traveling at a speed v along a straight line toward a wall. Assuming x to be a distance of the vehicle along the straight line from the wall, and assuming that the vehicle can brake, given any pair of distance and speed (x, v) and a maximum feasible braking, if x is too small and v is too high, then even with maximum allowed braking the vehicle will be unable to avoid a crash with the wall. As such, the set of all such pairs of distance and speed for which no control input exists that will avoid a crash with the wall is a capture set C for such a simple example and the role of the ICA system is to keep the vehicle out of the capture set and thereby avoid a crash of the vehicle with the wall by braking the vehicle before it is too late.”). Further, (See at least paragraph [0058] “For Case 4 both the local vehicle and the remote vehicle are within the capture set and thus no control can be made to prevent the vehicles from entering the bad set B. As such, no control of the vehicle is asserted by the ICA system but a strong warning is provided to the drivers”).
Shalev-Shwartz as modified by Febbo, and Hafner are analogous art because they are in the same field of endeavor, trajectory planning systems. Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Shalev-Shwartz to incorporate the teachings of Febbo because incorporating Febbo, and Hafner teachings with the own vehicles Trajectory and not just the target vehicles trajectory planning will aid in a more accurate collision avoidance system determining the host vehicle and target vehicle trajectory system
Regarding applicant claim 14, which is commensurate to claim 1, and although claim 14 further includes a addition limitation which Shalev-Shwartz discloses a plurality of sensors that determine a plurality of dynamic variables, ([0137] “sensors”; [0193]; [0263]).
Regarding applicant claim 2, and commensurate claim 15, and 20, Shalev-Shwartz as modified by Febbo, and Hafner discloses the claimed features of claim 1 an further discloses, wherein the function approximator approximates the set of real-time ego states in real-time based on a current position and a current velocity of the autonomous vehicle ([0091] “Such receivers can determine a user position and velocity”) and the one or more moving obstacles, a speed limit of a roadway that the autonomous vehicle is presently driving along, environmental variables, and road conditions. ([0170] “processing unit 110 may track the candidate objects across consecutive image frames, estimate the real-world position of the candidate objects, and filter out those objects that are moving”)
Regarding applicant claim 3, and commensurate claim 16, Shalev-Shwartz as modified by Febbo, and Hafner discloses the claimed features of claim 1 an further discloses, wherein the one or more controllers select the trajectory by: assigning a score to each relative state trajectory for the autonomous vehicle based on one or more properties; and selecting the relative state trajectory having the highest score as the trajectory. ([0285] “some constraints may be associated with greater safety risks than others and, therefore, may be assigned higher priorities.”).
Regarding applicant claim 4, Shalev-Shwartz as modified by Febbo, and Hafner discloses the claimed features of claim 3 and further discloses, wherein the one or more properties include one or more of the following: ride comfort, fuel consumption, timing, and duration. ([0364] “Such a situation may be undesirable from a safety perspective and may also result in discomfort to passengers of the host vehicle”).
Regarding applicant claim 5, Shalev-Shwartz as modified by Febbo, and Hafner discloses the claimed features of claim 1 and further discloses, further comprising a plurality of sensors in electronic communication with the one or more controllers, wherein the one or more controllers receive a plurality of dynamic variables as input from the plurality of sensors. ([0137] “sensors”; [0193]; [0263]).
Regarding applicant claim 6, Shalev-Shwartz as modified by Febbo, and Hafner discloses the claimed features of claim 5 and further discloses, wherein the one or more controllers determine the autonomous vehicle dynamics model for the autonomous vehicle based on the plurality of dynamic variables and vehicle chassis configuration information. ([0206] “model based learning and planning… dynamics of the process may be learned… and yields a distribution over the next states”).
Regarding applicant claim 7, Shalev-Shwartz as modified by Febbo, and Hafner discloses the claimed features of claim 1 and further discloses, wherein the position avoidance set is determined by: S={(es,ed)ER2:es,l<es<es,u,edl<ed<ed,uI wherein S is the position avoidance set, es is a discrete-time relative longitudinal state of the autonomous vehicle with respect to an obstacle, ed is a discrete-time relative lateral state of the autonomous vehicle with respect to the obstacle, es, is a lower limit of the discrete-time relative longitudinal state es,es, is an upper limit of the discrete-time relative longitudinal state es, ed,l is a lower limit of the discrete- time relative lateral state ed, and ed,u is a lower limit of the discrete-time relative longitudinal state es, of the position avoidance set S. ([0206] “model based learning and planning… dynamics of the process may be learned… and yields a distribution over the next states”).
Regarding applicant claim 9, Shalev-Shwartz as modified by Febbo, and Hafner discloses the claimed features of claim 1 and further discloses, wherein the one or more levels of driving aggression include a conservative level, a moderate level, and an aggressive level of aggression ([0352] “exceeding a threshold”).
Regarding applicant claim 10, Shalev-Shwartz as modified by Febbo, and Hafner discloses the claimed features of claim 1 and further discloses, wherein the one or more controllers determine the set of ego states during an offline process based on one of simulated data and experimental data. ([0371]).
Regarding applicant claim 11, Shalev-Shwartz as modified by Febbo, and Hafner discloses the claimed features of claim 1 and further discloses, wherein the one or more moving obstacles include another vehicle located along a roadway that the autonomous vehicle is driving along. ([0084] “one or more target vehicles in an environment of the host vehicle”).
Regarding applicant claim 12, Shalev-Shwartz as modified by Febbo, and Hafner discloses the claimed features of claim 1 and further discloses, wherein the set of ego states represent vehicle states where the autonomous vehicle is unable to execute maneuvers during a time horizon to avoid the one or more moving obstacles ([0368]-[0369]).
Regarding applicant claim 13, and commensurate claim 18, Shalev-Shwartz as modified by Febbo, and Hafner discloses the claimed features of claim 1 and further discloses, wherein the autonomous vehicle dynamics model includes one or more of the following: a linear tire model and non- linear tire models ([0304] “detection of failure or partial failure of… tires or any other system associated with the host vehicle that may impact the ability of the host vehicle to navigate relative to the navigational constraints”).
Regarding applicant claim 21, which is commensurate to claim 22, Shalev-Shwartz as modified by Febbo, and Hafner discloses the claimed features of claim 1, Shalev-Shwartz does not explicitly disclose, however Febbo discloses, wherein the plurality of relative state trajectories are calculated based on a multi-objective optimization between cumulative jerk and the one or more levels of driving aggression. (See [Column 9 Line 49-56] “if the vehicle travels too slowly, the system performance may not be as desired. If the trajectory planner 130 updates too quickly and the changes in the control signals are very small during the execution horizon, the vehicle may not move. Therefore, a minimum final time term makes the planner calculate more aggressive trajectories,”)
Shalev-Shwartz as modified by Febbo, are analogous art because they are in the same field of endeavor, trajectory planning systems. Therefore it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Shalev-Shwartz to incorporate the teachings of Febbo for the same motivation reasons in claim 1.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Wesam Almadhrhi whose telephone number is (571) 270-3844. The examiner can normally be reached on 7:30 AM - 5PM Mon-Fri Eastern Alt Fri.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Anne Antonucci can be reached on (313) 446-6519. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/WESAM NMN ALMADHRHI/Examiner, Art Unit 3666
/ANNE MARIE ANTONUCCI/Supervisory Patent Examiner, Art Unit 3666
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