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 .
Claim Status
Claims 1-2, 6, 8-10, 14, and 16-19 have been amended.
Claims 1-20 are pending.
Response to Arguments
Applicant’s arguments, see page 8, filed 08/04/2025, with respect to claims 1-20 rejections under 35 USC 101 have been fully considered and are persuasive. The 35 USC 101 rejections of claims 1-20 have been withdrawn.
Applicant's arguments filed with respect to claims 1-20 rejections under 35 USC 102(a)(1) have been fully considered but they are not persuasive. Applicant’s arguments pertain to newly amended limitations not addressed in the prior Office Action of record. Applicant argues that previously recited Li et al. (20220194424; hereinafter Li, already of record) fails to teach calculating a probabilistic uncertainty bound for a predicted path of a host vehicle in response to steering input and measurement noise, expanding the predicted path, generating a path area, and controlling the host vehicle in response to the path area.
The Examiner respectfully disagrees. Li recites:
“the overlap may be computed based on different lateral uncertainties, with each lateral uncertainty being representative of a risk tolerance for maneuverability of the AV ... This method may involve constructing a polygon including the agent's lateral bounds followed by computing the overlap between the extended polygon and the trajectory of the AV” ¶ 25,
“computing the probabilistic overlap is a two-step process where 1) a parallelogram corresponding to maximum uncertainty in the lateral position of the object is computed, and 2) the parallelogram is interpolated based on lateral bounds representative of a particular amount of risk tolerance for maneuvering the AV” ¶ 26,
“The AVCS 140 can also include a driving path selection system for selecting a particular path through the immediate driving environment” ¶ 42
“output by the sensor 206 and generate a number of return points of the second frame ... One or more mapping algorithms implemented by perception system 132 can determine a geometric transformation that maps the point cloud of the first frame onto the point cloud of the second frame. Such mapping can use the ICP algorithm which iteratively revises the transformation and minimizes an error metric (e.g., the mean squared error or some other pre-determined metric) ... for both object identification and tracking” ¶ 50
“the AV control system data representative of uncertainty in a lateral position of the object computed based on a relationship between the updated overlap region and the initial overlap region ... the AV control system is to modify the driving path or speed of the AV based on the data representative of the uncertainty in the lateral position” ¶ 69
Which does disclose of calculating a probabilistic uncertainty bound, expanding a path area based on the probabilistic uncertainty bound, such as expanding the path area within the environment as seen in paragraph 42, then controlling the vehicle to maintain the generated path area. A detailed rejection follows below.
Claim Rejections - 35 USC § 102
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Li et al. (20220194424; hereinafter Li).
Regarding claim 1, Li teaches a method for providing driving assistance in a host vehicle, comprising (Li: Abstract):
receiving, by a processor, vehicle data from a sensor system of the host vehicle (Li: Fig. 120, “The example AV 100 can include a sensing system 120. The sensing system 120 can include various electromagnetic (e.g., optical) and non-electromagnetic (e.g., acoustic) sensing subsystems and/or devices” ¶ 31, “The perception system 132 can further receive information from a GPS transceiver (not shown) configured to obtain information about the position of the AV relative to Earth ..” ¶ 39);
determining, by a processor, a predicted path of the host vehicle over a prediction horizon based on the vehicle data (Li: “The data processing system 130 can further include an environment monitoring and prediction component 136, which can monitor how the driving environment 110 evolves with time” ¶ 40);
calculating, by the processor, a probabilistic uncertainty bound for the predicted path of the host vehicle at a plurality of time steps, wherein the probabilistic uncertainty bound is based on a vehicle dynamic model associated with a steering maneuver type, an estimation of steering input characteristics and a measurement noise originating from a transient characteristic of a steering sensor (Li: “the overlap may be computed based on different lateral uncertainties, with each lateral uncertainty being representative of a risk tolerance for maneuverability of the AV ... This method may involve constructing a polygon including the agent's lateral bounds followed by computing the overlap between the extended polygon and the trajectory of the AV” ¶ 25, “computing the probabilistic overlap is a two-step process where 1) a parallelogram corresponding to maximum uncertainty in the lateral position of the object is computed, and 2) the parallelogram is interpolated based on lateral bounds representative of a particular amount of risk tolerance for maneuvering the AV” ¶ 26, “output by the sensor 206 and generate a number of return points of the second frame ... One or more mapping algorithms implemented by perception system 132 can determine a geometric transformation that maps the point cloud of the first frame onto the point cloud of the second frame. Such mapping can use the ICP algorithm which iteratively revises the transformation and minimizes an error metric (e.g., the mean squared error or some other pre-determined metric) ... for both object identification and tracking” ¶ 50);
expanding, by the processor, the predicted path of the host vehicle to a path area based on the probabilistic uncertainty bound (Li: “The AVCS 140 can also include a driving path selection system for selecting a particular path through the immediate driving environment” ¶ 42);
determining, by the processor, a target object based on the path area and object data that identifies objects within the environment of the host vehicle (Li: “a method comprises receiving, by a data processing system of an AV, data descriptive of an agent state of an object; computing an initial overlap region between a first box representative of a trajectory of the AV and a second box representative of the agent state; updating dimensions of the second box; and computing an updated overlap region by interpolating the initial overlap region based on the updated dimensions of the second box” ¶ 17);
generating, by the processor, a path control data for an adaptive cruise control system to control the host vehicle based on the target object (Li: “the AV control system data representative of uncertainty in a lateral position of the object computed based on a relationship between the updated overlap region and the initial overlap region ... the AV control system is to modify the driving path or speed of the AV based on the data representative of the uncertainty in the lateral position” ¶ 69); and
controlling the host vehicle by controlling a steering system, by the adaptive cruise control system, in response to the path control data (Li: “The control system can subsequently output instructions to powertrain and steering 150, vehicle electronics 160, signaling 170, etc., to ensure that the AV follows the determined driving path” ¶ 70, see also ¶ 44).
Regarding claim 2, Li teaches the method of claim 1, further comprising:
determining a steering maneuver based on the vehicle data (Li: “Algorithms and modules of AVCS 140 can generate instructions for various systems and components of the vehicle, such as the powertrain and steering 150” ¶ 43);
determining a vehicle model based on the steering maneuver (Li: “Some AV data processing systems and control systems utilize algorithms to predict how the objects overlap with the AV's trajectory in order to compute a safe driving trajectory” ¶ 24); and
wherein the determining the predicted path of the host vehicle is based on the vehicle model (Li: “Probabilistic overlaps can be computed between the AV and the agent state in order to estimate the risk of collisions between the AV and the object ... This method may involve constructing a polygon including the agent's lateral bounds followed by computing the overlap between the extended polygon and the trajectory of the AV” ¶ 25, see also ¶ 26, ¶ 65) and wherein the probabilistic uncertainty bound is calculated by determining a width of the path area at each of a plurality of points on the predicted path (Li: “constructing a polygon including the agent's lateral bounds followed by computing the overlap between the extended polygon and the trajectory of the AV” ¶ 25) in response to a minimum width (Li: “such as narrow streets, or crowded environments, the lateral bounds may be reduced (which is equivalent to higher risk tolerance)” ¶ 26), a tuning gain (Li: “One or more mapping algorithms implemented by perception system 132 can determine a geometric transformation ... iteratively revises the transformation and minimizes an error metric” ¶ 50), and a variance of a lateral position of the predicted path calculated over a time horizon (Li: “lateral bounds representative of a particular amount of risk tolerance for maneuvering the AV” ¶ 26).
Regarding claim 3, Li teaches the method of claim 2, wherein the steering maneuver includes at least one of a cornering maneuver, a turn maneuver (Li: “select an optimal driving strategy (e.g., braking, steering, accelerating, etc.) for avoiding the obstacles” ¶ 42, see also ¶ 44), a swerve maneuver, and a straight maneuver.
Regarding claim 4, Li teaches the method of claim 2, further comprising:
adapting the vehicle model based on feedback data associated with an actual vehicle path (Li: “the environment monitoring and prediction component 136 can make predictions about how various animated objects of the driving environment 110 will be positioned within a prediction time horizon ... he environment monitoring and prediction component 136 can perform periodic checks of the accuracy of its predictions and modify the predictions based on new data obtained from the sensing system 120” ¶ 40, see also ¶ 42, 45).
Regarding claim 5, Li teaches the method of claim 1, wherein the path comprises a plurality of points, and wherein expanding comprises using the probabilistic uncertainty bound prediction to determine a width perpendicular to the path at each point of the plurality of points, wherein the width is associated with at least one uncertainty (Li: “an AV trajectory 500 as a series of overlapping states in accordance with some implementations of the present disclosure. Each of AV states 502A, 502B, and 502C represent, for example, positions and orientations of the AV (which may be the same as or similar to AV 202) at different points in time, with the AV state 502A being the earliest point in time and the AV state 502C being the latest point in time ...” ¶ 57, “the query may specify a single lateral bound to update, for example, if the AV control system seeks to determine whether maneuvering the AV in a particular direction with respect to the object is feasible. In such implementations, the AV control system may cause the AV to maneuver in that direction if a lateral uncertainty is reduced as a result. In some implementations, a change in one or more of the lateral bounds of the agent state can range from 0.1 meters to about 2 meters” ¶ 67, see also ¶ 58).
Regarding claim 6, Li teaches the method of claim 5, wherein the at least one uncertainty includes the measurement noise (Li: “the return points in the second frame correspond to reflection surfaces of the object 210 that may be different from the surfaces causing reflections of the signals of the first frame. For example when parts of the rotating object 210 previously obscured come within a field of view of sensor 206, additional return points can be detected” ¶ 51, see also ¶ 49).
Regarding claim 7, Li teaches the method of claim 5, wherein the at least one uncertainty includes steering angle rate (Li: “object sensing to facilitate autonomous driving, e.g., distance sensing, velocity sensing, acceleration sensing, rotational motion” ¶ 31, “the perception system could have identified an object moving with the speed of 20 mph while making a left-hand turn with the radius of 15 m and communicated this information to the AV control system. The AV control system can then determine, based at least partially on lateral uncertainty of the object's position, that the AV is about to enter the same intersection before the object can complete the turn” ¶ 70, see also ¶ 38).
Regarding claim 8, Li teaches the method of claim 1, further comprising identifying at least one obstacle based on the path area and the object data that identifies objects within the environment of the host vehicle wherein the obstacle is outside of the predicted path area (Li: “scan the outside (relative to AV 302) environment. One sensing frame that corresponds to a single cycle of the transmitter/receiver 308 can produce multiple return points from various reflecting regions (depicted with black circles) of the object 310” ¶ 52, “different agent states at different times may overlap with the various AV states differently (partial overlap or smaller/larger overlap area) or not at all” ¶ 58, see also ¶ 56). 41
Regarding claim 9, Li teaches a system in a host vehicle for providing driving assistance (Li: “on-board data processing system of the AV” ¶ 23), comprising:
a controller configured to, by a processor (Li: “an AV control system (AVCS) 140” ¶ 41, “computer device 800 can include a processing device 802 (also referred to as a processor or CPU)” ¶ 72, see also ¶ 73):
...
In regards to the remainder of claim 9, the claim recites analogous limitations to claim 1, and is therefore rejected under the same premise.
Regarding claim 17, Li teaches a host vehicle comprising (Li: Fig. 1, “autonomous vehicle (AV) 100” ¶ 29):
a sensor system associated with a steering system (Li: “The sensing system 120” ¶ 33, “Signals obtained by various sensing devices can be used in conjunction with other data by an on-board data processing system of the AV ... utilized by an AV control system to determine how the AV is to safely and legally navigate the roadway” ¶ 23, see also ¶ 42);
an actuator system (Li: “various systems and components of the vehicle, such as the powertrain and steering 150, vehicle electronics 160, signaling 170, and other systems and components not explicitly shown in FIG. 1” ¶ 43);
a human machine interface (HMI) (Li: “Example computer device 800 can further comprise a video display 810 (e.g., a liquid crystal display (LCD), a touch screen, or a cathode ray tube (CRT)), an alphanumeric input device 812 (e.g., a keyboard), a cursor control device 814 (e.g., a mouse), and an acoustic signal generation device 816 (e.g., a speaker)” ¶ 74); and
a controller for implementing a driver assistance system (Li: “an AV control system (AVCS) 140” ¶ 41), the controller configured to:
...
In regards to the remainder of claim 17, the claim recites analogous limitations to claim 1, and is therefore rejected under the same premise.
In regards to claim(s) 10-16, the claim(s) recite analogous limitations to claim(s) 2-8, and are therefore rejected under the same premise.
In regards to claim(s) 18-20, the claim(s) recite analogous limitations to claim(s) 5-7, and are therefore rejected under the same premise.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Yu et al. (20220355819) is in the similar field of endeavor as the claimed invention of vehicle path prediction.
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 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.
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/C.P./ Examiner, Art Unit 3663
/ABBY J FLYNN/ Supervisory Patent Examiner, Art Unit 3663
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