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 .
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. CN202410628313.5, filed on 05/20/2024.
Status of Claims
This office action is in response to application number 19/213,439 filed on 05/20/2025, in which claims 1-20 are presented for examination.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 11, & 12 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Beaudoin et al. US 20220101155 A1 (hereinafter Beaudoin).
Claim 1: Beaudoin discloses A control method for a vehicle, comprising: acquiring a first image captured by a camera with a preset viewing angle mounted on the vehicle [[0014] - [0015]; the sensor data includes at least one of a camera image or a point cloud from a light detection and ranging (LiDAR) sensor captured at the location. (…) the sensor data includes data output by a perception module of the vehicle]; transforming the first image into a second image in bird's-eye view [[0109]; The input image is generated by rasterizer 602 that converts sensor data 610 capturing a scene (e.g., camera images, LiDAR point cloud) into an input image, such as a bird's eye view (BEV) image]; determining, based on the second image, a first predicted driving trajectory of the vehicle in a local coordinate system [[0062]; scene data is rasterized into an image (e.g., a bird's eye view (BEV) image), which is processed by the attention mechanism that compiles a local summary of those portions of the BEV image that are relevant to the trajectory template, and encodes the compiled local summary into a local feature vector]; correcting the first predicted driving trajectory based on observation information corresponding to the first image to obtain a second predicted driving trajectory [[0126]; the feature vector and the sensor data are provided as input into a refinement model that outputs adjustments to the at least one trajectory template. Some examples of the adjustments include increasing or decreasing the speed of one or more agents at the location, and laterally displacing one or more agents from a centerline of a road at the location due to, for example, a parked vehicle on the side of the lane]; and controlling a driving state of the vehicle based on the second predicted driving trajectory [[0099]; The control module 406 receives the data representing the trajectory 414 and the data representing the AV position 418 and operates the control functions 420a-c (e.g., steering, throttling, braking, ignition) of the AV in a manner that will cause the AV 100 to travel the trajectory 414 to the destination 412].
Claim(s) 11: The claim(s) is directed towards an apparatus of the recited limitations performed by the method of claim(s) 1, respectively. The cited portions of Beaudoin used in the rejection of claim(s) 1 teach the same steps to perform the apparatus of claim(s) 11, respectively. Therefore, claim(s) 11 is rejected under the same rationales used in the rejection of claim(s) 1 as outlined above.
Claim(s) 12: The claim(s) is directed towards an apparatus of the recited limitations performed by the method of claim(s) 1, respectively. The cited portions of Beaudoin used in the rejection of claim(s) 1 teach the same steps to perform the apparatus of claim(s) 12, respectively. Therefore, claim(s) 12 is rejected under the same rationales used in the rejection of claim(s) 1 as outlined above.
Allowable Subject Matter
Claims 2-10 & 13-20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See PTO-892.
Sota et al. (US 20200125861 A1) discloses a road line detection device includes a processor configured to: calculate, for each pixel of an image acquired by a camera mounted on a vehicle, a confidence score that a road line is represented in the pixel, and a confidence score that another object is represented in the pixel; set a correction region in a range assumed to include a road line in the image during changing lanes; correct, for each pixel included in the correction region, the confidence score for a road line or the confidence score for another object in such a way that the confidence score for a road line is high relative to the confidence score for another object; and detect a road line from the image based on each pixel in which the confidence score for a road line is higher than the confidence score for another object.
Chen et al. (US 20230334876 A1) discloses a method for an end-to-end boundary lane detection system is described. The method includes gridding a red-green-blue (RGB) image captured by a camera sensor mounted on an ego vehicle into a plurality of image patches. The method also includes generating different image patch embeddings to provide correlations between the plurality of image patches and the RGB image. The method further includes encoding the different image patch embeddings into predetermined categories, grid offsets, and instance identifications. The method also includes generating lane boundary keypoints of the RGB image based on the encoding of the different image patch embeddings.
Del Pero et al. (US 20210403001 A1) discloses a computing system that is operable to (i) identify a set of vehicle trajectories that are associated with a segment of a road network, (ii) identify a first cluster of sampling points between the identified set of vehicle trajectories and a first sampling position along the segment, wherein the first cluster has an associated geospatial position and is inferred to be associated with one given lane of the segment, (iii) identify a subset of vehicle trajectories in the identified set that are inferred to be associated with the given lane between the first sampling position and a second sampling position along the segment, (iv) identify a second cluster of sampling points between the identified subset of vehicle trajectories and the second sampling position, wherein the second cluster has an associated geospatial position, and (v) determine a geospatial geometry of the given lane.
Kobilarov et al. (US 12391246 B2) discloses determining optimal driving trajectories for autonomous vehicles in complex multi-agent driving environments. A baseline trajectory may be perturbed and parameterized into a vector of vehicle states associated with different segments (or portions) of the trajectory. Such a vector may be modified to ensure the resultant perturbed trajectory is kino-dynamically feasible. The vectorized perturbed trajectory may be input, including a representation of the current driving environment and additional agents, into a prediction model trained to output a predicted future driving scene. The predicted future driving scene, including predicted future states for the vehicle and predicted trajectories for the additional agents in the environment, may be evaluated to determine costs associated with each perturbed trajectory. Based on the determined costs, the optimization algorithm may determine subsequent perturbations and/or the optimal trajectory for controlling the vehicle in the driving environment.
Wang et al. (US 20250227205 A1) discloses a method includes obtaining image frames from each camera disposed along a vehicle, where each image frame corresponds to a same timestamp. The method further includes constructing a first birds-eye view (BEV) image from each image frame with a first BEV module and constructing a second BEV image from each image frame by Inverse Perspective Mapping (IPM) with a second BEV module. The first BEV module extracts features of an external environment of the vehicle from each image frame, transforms the features to a three-dimensional space, and projects the three-dimensional space onto an overhead two-dimensional plane. Subsequently, a merging module merges the first and second BEV images to produce a hybrid BEV image. Features of an external environment of the vehicle within the hybrid BEV image are detected by a deep learning neural network and the hybrid BEV image is displayed to a user in the vehicle.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Anthony G Mora whose telephone number is (571)272-2306. The examiner can normally be reached Monday thru Thursday 8am-5pm PST, Alternating Friday 8am-4pm PST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kito R Robinson can be reached at (571)270-3921. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANTHONY GABRIEL MORA/Examiner, Art Unit 3664
/KITO R ROBINSON/Supervisory Patent Examiner, Art Unit 3664
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