DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claims 1-20 are pending in this office action.
Claims 1, 3-5, 15 and 19-20.
Response to Arguments
Applicant's arguments filed 10/17/2025 have been fully considered but they are not persuasive.
Applicant’s argument:
Heit describes "comparing performance metrics 112 determined from several scenarios with identical scenario parameters 110 and incrementally varying vehicle parameters 114" (emphasis added). Parameterizations with identical scenario parameters, as described by Heit, are not "neighboring parameterizations of the scenario," as recited in amended independent claim 1. Thus, Heit's description of comparing performance metrics from scenarios "with identical scenario parameters" would not have disclosed "comparing a test result computed for the first target parameterization of the scenario with respective test results computed for a first subset of neighboring parameterizations of the scenario," as recited in amended independent claim 1.
Examiner’s response:
The issue in the argument is that Heit does not discloses comparing results between scenario instance that are within the vicinity of the parameters, and further more Heit only discloses comparing for identical parameters.
First let start from specification to understand what is scenario., instances of scenario with space parameters and the comparison:
“ A "parameterization" of a scenario refers to a particular (combination
of) parameter value(s), corresponding to a point in a "parameter space" of the scenario (each configurable parameter defines a dimension of the parameter space). The following examples consider scenarios with multiple configurable parameters, but it will be appreciated that the description applies equally to the single parameter case”;
in Height [0040-0043], a scenario with a set of parameters as indicated by the applicant’s argument is simulated. Simulation by varying scenario parameters values( dimension space) between successive scenario. To more emphasize, Heit [0025] discloses the following:
[0025]” As yet another example, scenario parameters 110 may describe a scenario of the autonomous vehicle system in which the vehicle is traveling at 40 MPH on a busy city road, and a cyclist enters the vehicle's path at a distance of 100 feet and attempts to avoid the vehicle by swerving to the right five seconds later. Scenario parameters 110 may be incrementally changed to describe scenarios with the autonomous vehicle traveling at incrementally increasing speeds when the cyclist enters its path, such as speeds increased to each of 55 MPH, 60 MPH, 65 MPH, and 70 MPH, and with the scenario of each speed of the autonomous vehicle corresponding to scenario parameters 110 describing it.”;
Above the scenario has a set of parameters(speed, distance…), to create instances of the scenario but with different values(identical parameters but with different value(dimension)), for example speed increases/increments : 55, 60, 65 and 70,
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To summarize it is a set of parameters in which corresponding value varies , but not identical value.
In response to comparing the results of different scenario, to more emphasize 0025 of Heit discloses the following:
“In those examples, the results of the autonomous vehicle's attempts to avoid the cyclist may be evaluated (e.g., processor 102 may determine performance metrics 112 corresponding to the vehicle in each scenario, where such performance metrics can reflect whether the vehicle collided with the cyclist, whether the vehicle caused unsafe driving conditions for other vehicles due to the avoidance maneuver, etc.) for each of the speeds of the autonomous vehicle corresponding to the various scenarios.”;
In other word:
For speed 40 there is an associated result
For speed 55 there is a corresponding result..etc.
Among the scenario and its corresponding result one scenario may have specific result: minimum performance:
[0032] “Further, minimum performance scenarios 116 may be determined to be a subset of the scenarios of the autonomous vehicle system, and may be determined according to an analysis of performance metrics 112 from a variety of scenarios with differing scenario parameters 110.”;
Once the minimum performance is determined, the results of neighboring scenario are also evaluated in relation to the scenario of the minimum performance either as improving or falling down :
[0044] “ In many examples, system 100 may determine performance metrics 112 of the minimum performance scenario(s) as it varies vehicle parameters 114 to determine whether changing vehicle parameters 114 results in any changes in the performance metrics 112 (e.g., improvements of the performance metrics in those scenarios). Additionally, system 100 may determine performance metrics 112 for each of the variety of scenarios with unique vehicle parameters 114. In certain examples, system 100 may improve the vehicle parameters 114, or determine improved vehicle parameters 114, based on the performance metrics 112 of the scenarios having incrementally varying vehicle parameters 114 at step 210”;
Applicant’s argument:
in 0043, Heit describes comparing "various scenario parameters" to Second, "predetermined threshold values." Predetermined threshold values, as described by Heit, are not "neighboring parameterizations of the scenario," as recited in amended independent claim 1. Thus, Heit's description of comparing scenario parameters with "predetermined threshold values" would not have disclosed "comparing a test result computed for the first target parameterization of the scenario with respective test results computed for a first subset of neighboring parameterizations of the scenario," as recited in amended independent claim 1.
Examiner response:
The issue in the argument is that comparing results to a threshold is not comparing results to each other;
[0043] threshold is used to determine the minimum performance result . once determined the result of vicinity scenario are compared to the minimum performance scenario to check if improved performance(optimization) is detected [0040].
To more emphasize
[0055] In some examples, system 100 may determine step size in scenario parameters 110 based on whether changes in performance metrics 112 of the previous scenario exceeded specified threshold amounts. For example, the threshold amount of change in performance metrics 112 may be an average change in performance metrics 112 previously determined from several implementations of process 300. Moreover, the amount of change in scenario parameters 110 may decrease for an amount of change in performance metrics 112 that exceeds the threshold (or average) amount, and where the amount of change in performance metrics 112 is less than the threshold amount, the amount of change in scenario parameters 110 may increase.
Above the threshold is a measure of the scenario to the previous scenario in detecting performance optimization.
In response to Hawthorne:
Hawthorne also is directed to autonomous driving vehicle simulation with different scenario.
[0028] “ As such, the mission associated with FIG. 1 is to travel from the launch point to the waypoint and then to the recovery point. As indicated above, a dynamic environmental parameter in the state space 100 is the (X,Y) position of the pentagonal obstacle 102. As further described below, a test scenario may be run through the autonomy with the obstacle 102 at one position and the results of the run may be scored in accordance with defined performance scoring metrics. Different scenarios may then be run with the dynamic parameter, i.e., the position of the obstacle 102, at a different value to determine additional performance scoring metric values for the various runs. In the example shown in FIG. 1 for a given autonomy, the decisions made by the software for a run where obstacle 102 is centered at (700, 1700) may cause a vehicle to navigate along path 114 where the vehicle reaches both the waypoint 110 and the recovery point 112.”
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Heit et al US20190155291A1 in view of Hawthorne et al US 20190179738A1.
As per claim 1, Heit discloses a computer-implemented method of evaluating a performance of a trajectory planner in simulation:
[0052] “As explained in greater detail above with reference to FIG. 1, performance metrics 112 determined at step 310 may indicate the quality of the autonomous vehicle system or of a driving maneuver attempted by the autonomous vehicle system and/or a driver of the autonomous vehicle system (e.g., an automatic braking maneuver, autonomous lane change, driver assisted cruise control, among other possibilities).”;
the method comprising:
running first instances of a scenario in a simulator, the first instances run with a first set of parameterizations of the scenario, the trajectory planner used to control an ego agent responsive in each scenario instance:
[0041] At 202, system 100 can perform a scenario analysis by simulating several scenarios, and determining performance metrics 112 of the scenarios. In one example, as part of step 202, system 100 may simulate a variety of scenarios of the autonomous vehicle system, with vehicle parameters 114 of each simulation being identical, and the scenario parameters 110 describing each scenario changing incrementally with each subsequent scenario, as described with reference to scenario parameters 110 of FIG. 1. “;
[0042]” For example, system 100 could determine performance metrics 112 of the scenarios as a braking distance, and may determine an average braking distance (distance required for the autonomous vehicle system to slow to a stop) for a speed of 65 MPH in several scenarios to be 30 meters and a standard deviation in braking distance to be 5 meters.”;
evaluating performance of the trajectory planner in each first scenario instance, thereby computing a first set of test results for the first set of parameterizations of the scenario:
[0042] “In this example, system 100 may determine minimum performance scenarios 116 as scenarios with performance metrics 112 indicating a braking distance greater than the average of 30 meters by a standard deviation (5 meters) or more. Alternatively, or in addition, system 100 could determine minimum performance scenarios to be scenarios with braking distances exceeding the average of 30 meters by two standard deviations or more (10 meters), among other possible methods for determining minimum performance scenarios 116”;
identifying at least one first target parameterization of the scenario included in the first set of parameterizations of the scenario based on the first set of test results, by comparing a test result computed for the first target parameterization of the scenario with respective test results computed for a first subset of neighboring parameterizations of the scenario included in the first set of parameterizations of the scenario:
[0043]“In certain examples, process 200 may include step 206, so that system 100 determines a likely cause for one or more of the minimum performance scenarios 116 of step 204. For example, if luminosity of shining sunlight is a scenario parameter 110 of a minimum performance scenario 116, then system 100 may determine that a likely cause of the minimum performance scenarios 116 is overexposure of one or more of the autonomous vehicle's sensors. In some examples, a likely cause of one or more minimum performance scenario 116 may be determined based on one or more predetermined threshold values of the various scenario parameters. For example, failing to detect an object appearing less than one foot before a simulated vehicle may be attributed to known minimum sensing distance as a likely cause of the minimum performance scenario 116”;
[0040] “As a general example, system 100 may perform process 200 by simulating several scenarios of the autonomous vehicle system and incrementally varying scenario parameters 110 between successive scenarios, determining performance metrics 112 of the various scenarios, determining minimum performance scenarios 116 as a subset of the scenarios, and improving vehicle parameters 114 by comparing performance metrics 112 determined from several scenarios with identical scenario parameters 110 and incrementally varying vehicle parameters 114 to optimize vehicle parameters and performance, as will be described in more detail below”; See also [0044] 0054-0055 and 0065.
based on the first target parameterization of the scenario, determining a second set of parameterizations of the scenario for running second instances of the scenario for exploring a first subspace of the parameter space in a vicinity of the first target parameterization of the scenario:
[0044] “At 208, system 100 may perform a vehicle analysis, and may simulate several configurations of the autonomous vehicle system in the minimum performance scenario(s) 116 determined at 204 by incrementally changing vehicle parameters 114. As described in greater detail above with reference to vehicle parameters 114 of FIG. 1, vehicle parameters 114 can define the configuration of the autonomous vehicle system of a scenario, such as the vehicle's sensor configuration, make, model, tires and/or vehicle mileage, among other possibilities. In many examples, system 100 may determine performance metrics 112 of the minimum performance scenario(s) as it varies vehicle parameters 114 to determine whether changing vehicle parameters 114 results in any changes in the performance metrics 112 (e.g., improvements of the performance metrics in those scenarios)”;
But not explicitly:
wherein the first subset of neighboring parameterizations of the scenario neighbor the first target parameterization of the scenario in a parameter space of the scenario:
Hawthorne discloses:
wherein the first subset of neighboring parameterizations neighbor the first target parameterization in a parameter space of the scenario:
[0045] “ In this regard, the scenario configuration state space may be defined as x.sup.n=[x.sub.1, . . . , x.sub.n] having n elements (or parameters). Each element in the state space vector represents a variable in the environment, mission, or vehicle parameters with a range of possible values (obstacle positions, time windows, mission priorities, etc.). The state space may be a continuous, real-valued metric space where the distance between points (i.e., scenarios) may represent the similarity between their configurations or similarity between their collective elements or parameters. To accommodate such requirements, a scenario generator function κ:X.fwdarw.S.sub.0 may be defined which maps the state space into a specific input state utilized by the simulation environment. This enables application of the system to scenarios with categorical parameters to generate values for non-uniform distributions.’;
See also 0046-0048 for parameter spaces.
It would have obvious to one having ordinary skill in the art before the effective filling date of the claimed invention to combine the teachings of cited references. One of ordinary skill in the art before the effective filling date of the claimed invention would have been motivated to incorporate the teachings of Hawthorne into teachings of Heit to enable eliminating the need for a user to define objective function that can be difficult to design and can require careful tuning of scoring parameters. The method enables reducing range and dimensionality of state space to result in more efficient and effective search by applying sensitivity analysis techniques only over state . the use of an adaptive search to select scenarios for simulation significantly reduces the number of scenario simulation runs that need to be performed to develop an understanding of the performance of the autonomy under test. [Hawthorne0023].
As per claim 2, the rejection of claim 1 is incorporated and furthermore Heit discloses:
exploring the first subspace of the parameter space by running second instances of the scenario in the simulator with the second set of parameterizations:
[0044] “In many examples, system 100 may determine performance metrics 112 of the minimum performance scenario(s) as it varies vehicle parameters 114 to determine whether changing vehicle parameters 114 results in any changes in the performance metrics 112 (e.g., improvements of the performance metrics in those scenarios)”;
and evaluating the performance of the trajectory planner in each second scenario instance, thereby computing a second set of test results for the second scenario instances:
[0044] “Additionally, system 100 may determine performance metrics 112 for each of the variety of scenarios with unique vehicle parameters 114. In certain examples, system 100 may improve the vehicle parameters 114, or determine improved vehicle parameters 114, based on the performance metrics 112 of the scenarios having incrementally varying vehicle parameters 114 at step 210. For example, if given changes in vehicle parameters cause improvements in the performance metrics 112 of interest, system 100 can identify those vehicle parameters as improved vehicle parameters associated with the minimum performance scenario(s)”;
As per claim 3, the rejection of claim 1 is incorporated and furthermore Heit discloses:
identifying at least one second target parametrization of the second set is identified in a same way, by comparing a test result computed for the second target parameterization with respective test results computed for a second subset of neighboring parameterizations:
[0046] “Additional examples of criteria for determining whether to forgo additional iterations of process 200 may include: whether a previously specified number of iterations of process 200 has occurred, whether vehicle parameters 114 were substantially improved, whether improved vehicle parameters 114 resulted in unexpected minimum performance scenarios 116, among other possibilities. In some examples, system 100 may determine to forgo performing an additional iteration of process 200, and system 100 may perform step 216 by ending process 200”;
wherein the second subset of neighboring parameterizations neighbor the first target parameterization in a parameter space of the scenario:
[0044] “In certain examples, system 100 may improve the vehicle parameters 114, or determine improved vehicle parameters 114, based on the performance metrics 112 of the scenarios having incrementally varying vehicle parameters 114 at step 210.
wherein the second subset of neighboring parametrizations is a subset of the second set of parameterizations or a subset of the first set of parameterizations and the second set of parameterizations combined:
[0044] “In many examples, system 100 may determine performance metrics 112 of the minimum performance scenario(s) as it varies vehicle parameters 114 to determine whether changing vehicle parameters 114 results in any changes in the performance metrics 112 (e.g., improvements of the performance metrics in those scenarios). Additionally, system 100 may determine performance metrics 112 for each of the variety of scenarios with unique vehicle parameters 114.”;
and based on the second target parameterization, determining a third set of parameterizations of the scenario for running third instances of the scenario for exploring a second subspace of the parameter space in the vicinity of the second target parameterization:
[0045] “In certain examples, process 200 may include step 212, so that system 100 performs a scenario analysis, as described for step 202 above, but with the improved vehicle parameters 114 of step 210, rather than the initial vehicle parameters of step 202. In many examples, system 100 may perform step 212 by testing, verifying, and/or confirming whether an improved vehicle configuration from step 210 results in any new minimum performance scenarios 116. For example, improved vehicle parameters 114 may include a wider tire base, and system 100 may determine performance metrics 112 for the wider tire base (i.e., the improved vehicle parameters) to determine whether unexpected minimum performance scenarios 116 exist, such as a sharp decrease in fuel economy of the autonomous vehicle system”;
Examiner interpretation: a set of an initial parameters is used and after each iteration the set is modified by changing the initial parameters values such as above in [0045] or fig.2 and 3.
As per claim 4, the rejection of claim 2 is incorporated and furthermore Heit discloses:
wherein the second instances are run automatically in response to the identifying of the first target parameterization, or in response to a user input at a user interface:
[0045]“In many examples, system 100 may perform step 212 by testing, verifying, and/or confirming whether an improved vehicle configuration from step 210 results in any new minimum performance scenarios 116”;
As per claim 5, the rejection of claim 3 is incorporated and furthermore Heit discloses:
wherein the second and third instances are run automatically in response to the identifying of the first target parameterization, or in response to a user input at a user interface:
[0045]“In many examples, system 100 may perform step 212 by testing, verifying, and/or confirming whether an improved vehicle configuration from step 210 results in any new minimum performance scenarios 116”;
and wherein the method continues running instances iteratively until a terminating condition is satisfied:
[0046] “As another specific example, system 100 may determine whether to end process 200 based on a confidence index of the results of steps 204, 206, and 208, where the confidence index indicates a statistical reliability of the results of steps 204, 206 and 208. Additional examples of criteria for determining whether to forgo additional iterations of process 200 may include: whether a previously specified number of iterations of process 200 has occurred, whether vehicle parameters 114 were substantially improved, whether improved vehicle parameters 114 resulted in unexpected minimum performance scenarios 116, among other possibilities. In some examples, system 100 may determine to forgo performing an additional iteration of process 200, and system 100 may perform step 216 by ending process 200”;
As per claim 6, the rejection of claim 1 is incorporated and furthermore Heit discloses:
wherein the first target parameterization is identified by detecting one or more discrepancies between the test result of the first target parameterization and the respective test results of the first subset of neighboring parameterizations:
[0043]“In certain examples, process 200 may include step 206, so that system 100 determines a likely cause for one or more of the minimum performance scenarios 116 of step 204. For example, if luminosity of shining sunlight is a scenario parameter 110 of a minimum performance scenario 116, then system 100 may determine that a likely cause of the minimum performance scenarios 116 is overexposure of one or more of the autonomous vehicle's sensors. In some examples, a likely cause of one or more minimum performance scenario 116 may be determined based on one or more predetermined threshold values of the various scenario parameters”;
As per claim 7, the rejection of claim 6 is incorporated and furthermore Heit discloses:
wherein the first target parameterization is identified by determining that the test result of the first target parameterization differs from each test result of more than a predetermined number of the first subset of neighbouring parameterizations.
[0035] “In some examples, scenario analyzer 120 analyzes performance metrics 112 of various scenarios of the autonomous vehicle system to determine which subset of the scenarios are minimum performance scenarios 116. For example, scenario analyzer 120 may compare performance metrics 112 of several scenarios, and may determine whether one or more performance metrics 112 indicate a quality of operation or of a maneuver attempted by the autonomous vehicle that is less than a specified quality threshold”;
As per claim 8, the rejection of claim 1 is incorporated and furthermore Heit discloses:
wherein the performance of the trajectory planner is evaluated based on one or more predetermined trajectory evaluation rules:
[0043] “In some examples, a likely cause of one or more minimum performance scenario 116 may be determined based on one or more predetermined threshold values of the various scenario parameters. “;
As per claim 9, the rejection of claim 8 is incorporated and furthermore Heit discloses:
wherein the one or more predetermined trajectory evaluation rules pertain to safety, comfort, progress towards a defined goal, or any combination thereof:
[0018] “ For example, vehicle control systems, optimal vehicle parameters (e.g., sensor positions), and other information can be retrieved from systems described herein to create a safe and reliable autonomous vehicle”;
As per claim 10, the rejection of claim 8 is incorporated and furthermore Heit discloses:
wherein each test result is categorical:
[0052]“performance metrics 112 determined at step 310 may indicate the quality of the autonomous vehicle system or of a driving maneuver attempted by the autonomous vehicle system and/or a driver of the autonomous vehicle system (e.g., an automatic braking maneuver, autonomous lane change, driver assisted cruise control, among other possibilities). Moreover, and also described above with reference to FIG. 1, some examples of performance metrics 112 may include one or more of: a length of a delay before the autonomous vehicle system senses an object within range of a specified sensor (e.g., delay of 300 milliseconds for the vehicle to detect an object via LIDAR, delay of 800 milliseconds to detect an object via radar, etc.), a braking distance (e.g., requiring 50 meters, 75 meters, 100 meters, etc. to come to a complete stop), a distance between a specified sensor and an object at which the sensor can detect the object (e.g., a LIDAR sensor able to sense an object at distances of 200 meters or less, a radar sensor able to sense an object at distances of 500 meters or less, etc.), a delay between receiving data and engaging one or more vehicle systems to react to the data(e.g., 500 millisecond delay between detecting stopped traffic and engaging the vehicle's brakes,”;
As per claim 11, the rejection of claim 10 is incorporated and furthermore Heit discloses:
wherein each test result is computed from a numerical performance score based on at least one threshold:
[0043]“In some examples, a likely cause of one or more minimum performance scenario 116 may be determined based on one or more predetermined threshold values of the various scenario parameters.”;
As per claim 12, the rejection of claim 1 is incorporated and furthermore Heit discloses:
wherein the second set of parameterizations is outputted to a user, via a user interface, for manually instigating the second instances of the scenario:
[0036] “In some examples, performance metric analyzer 122 can determine performance metrics 112 of the scenarios of the autonomous vehicle system. In some examples, performance metric analyzer 122 may determine performance metrics 122 according to user input, such as a user selecting preferred kinds of performance metrics 112 to determine in one or more scenarios by using a keyboard and mouse to select from a list of several types of performance metrics 112”;
[0058]”Graph 320 illustrates several points 322, 324, and 326 that system 100 may correspond to scenario parameters and performance metrics of three minimum performance scenarios 116, as described in greater detail above with reference to FIG. 1 (e.g., points 322, 324 and 326 can correspond to scenarios in which the current vehicle configuration performance most poorly according to the current performance metric).
“;
As per claim 13, the rejection of claim 1 is incorporated and furthermore Heit
discloses:
wherein a test result is computed for each parameterization of the first set of parameterizations from a single first scenario instance or multiple first scenario instances:
[0066] Some examples may determine step size of, or the amount of change in, vehicle parameters based on a slope of the performance metrics 112 determined during process 400, such as a slope between two points of performance metrics 112 graphed as a function of vehicle parameters 110 in FIG. 4B. As an example, system 100 may determine step size in vehicle parameters 114 between two scenarios during process 400 based on whether the amount of change in the performance metrics 112 of previous scenarios during process 400 exceeded a specified threshold amount. For example, the threshold amount of change in the performance metrics 112 of scenarios during process 400 may be an average amount of change in the performance metrics 112 of the scenarios simulated during process 400 up to that point, or as determined from several previous implementations of process 400”;
As per claim 14, the rejection of claim 13 is incorporated and furthermore Heit
discloses:
wherein the simulator is non-deterministic:
[0066] “ Alternatively or in addition, some examples of process 400 may include determining changes in vehicle parameters 114 according to one or more statistical methods. For example, system 100 may simulate a fixed number of scenarios of the autonomous vehicle system automatically braking while traveling down an incline, and may randomly determine the weight of the autonomous vehicle system in each scenario according to a normal or Gaussian distribution of the vehicle's weight (i.e., the vehicle parameters) that is centered at 3,000 pounds”;
wherein multiple first scenario instances are run for each first parameterization, and wherein the test result for each first parameterization is an aggregate test result for the multiple first scenario instances.
[0066]”For example, the threshold amount of change in the performance metrics 112 of scenarios during process 400 may be an average amount of change in the performance metrics 112 of the scenarios simulated during process 400 up to that point, or as determined from several previous implementations of process 400”;
As per claim 15, the rejection of claim 1 is incorporated and furthermore Heit
discloses:
wherein the second set of parameterizations has a higher density in the first subspace of the parameter space than the first set of parameterizations.
[0053] “ Alternatively, or in addition, in some examples the last scenario may be simulated only after simulating a specified number of scenarios and only after simulating scenarios with specified scenario parameters 110. For example, system 100 may forgo simulating further scenarios of the autonomous vehicle system navigating various amounts of traffic in response to determining that thirty scenarios have been simulated, and that scenarios with the little or no vehicle traffic, and scenarios with very congested traffic have both been simulated.
As per claim 16, the rejection of claim 15 is incorporated and furthermore Heit
discloses:
wherein the first set of parameterizations are uniformly spaced in the parameter space with a first uniform density, and wherein the second set of parameterizations are uniformly spaced with a second uniform density greater than the first uniform density.
[0058] Graph 320 illustrates several points 322, 324, and 326 that system 100 may correspond to scenario parameters and performance metrics of three minimum performance scenarios 116, as described in greater detail above with reference to FIG. 1 (e.g., points 322, 324 and 326 can correspond to scenarios in which the current vehicle configuration performance most poorly according to the current performance metric). In some examples, point 328 illustrates a scenario in which scenario parameters were outside of a known operational range of the autonomous vehicle system, and as a result may be disregarded”.
As per claim 17, the rejection of claim 1 is incorporated and furthermore Heit
discloses:
wherein the trajectory planner is tested in combination with a controller, a perception system, and/or a prediction system.
[0038] “ In some examples, simulator 126 and simulation controller 128 may simulate the various scenarios according to scenario parameters 110 and vehicle parameters 114 as determined by scenario analyzer 120 and vehicle analyzer 124.”;
As per claim 18, the rejection of claim 1 is incorporated and furthermore Heit
discloses:
wherein the trajectory planner is used to control the ego agent responsive to at least one other agent in each scenario instance.
[0025] “….As yet another example, scenario parameters 110 may describe a scenario of the autonomous vehicle system in which the vehicle is traveling at 40 MPH on a busy city road, and a cyclist enters the vehicle's path at a distance of 100 feet and attempts to avoid the vehicle by swerving to the right five seconds later…”;
Claim 19 is the method claim corresponding to method claim 1 and rejected under the same rational set forth in connection with the rejection of claim 1 above.
Claim 20 is the non-transitory computer-readable storage media claim corresponding to method claim 1 and rejected under the same rational set forth in connection with the rejection of claim 1 above.
Pertinent arts:
US 20220048533 A1:
simulating use of the ACS in agent movement scenarios, wherein said simulating includes providing input sensor data to each vehicle agent to which an ACS has been assigned, and wherein movement of each of the other agents in the simulation is controlled to move either according to a replay of a corresponding real-world agent, or by an agent movement model.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRAHIM BOURZIK whose telephone number is (571)270-7155. The examiner can normally be reached Monday-Friday (8-4:30).
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Wei Y Mui can be reached at 571-270-2738. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRAHIM BOURZIK/Examiner, Art Unit 2191 /WEI Y MUI/Supervisory Patent Examiner, Art Unit 2191
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