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    METHOD AND SYSTEM FOR VALIDATING AUTONOMOUS VEHICLE PERFORMANCE USING NEARBY TRAFFIC PATTERNS
    23.
    发明公开
    METHOD AND SYSTEM FOR VALIDATING AUTONOMOUS VEHICLE PERFORMANCE USING NEARBY TRAFFIC PATTERNS 审中-公开

    公开(公告)号：US20230343210A1

    公开(公告)日：2023-10-26

    申请号：US17725814

    申请日：2022-04-21

    Applicant: GM Global Technology Operations LLC

    Inventor： Wei Tong ,  Shige Wang ,  Shuqing Zeng ,  Sourav Dey ,  Xiaofeng Frank Song ,  Upali P. Mudalige ,  Praveen Choudhury

    IPC: G08G1/01

    CPC classification number: G08G1/0145 ,  G08G1/0141 ,  G08G1/0133

    Abstract: A method for validating an autonomous vehicle performance using nearby traffic patterns includes receiving remote vehicle data. The remote vehicle data includes at least one remote-vehicle motion parameter about a movement of a plurality of remote vehicles during a predetermined time interval. The method further includes determining a traffic pattern of the plurality of remote vehicles using the at least one remote-vehicle motion parameter. The method includes determining a similarity between the traffic pattern of the plurality of remote vehicles and movements of the host vehicle. Further, the method includes determining whether the similarity between the traffic pattern of the plurality of remote vehicles and movements of the host vehicle is less than a predetermined threshold. Also, the method includes commanding the host vehicle to adjust the movements thereof to match the traffic pattern of the plurality of remote vehicles.

    PERCEPTION PROCESSING WITH MULTI-LEVEL ADAPTIVE DATA PROCESSING FLOW RATE CONTROL
    24.
    发明申请
    PERCEPTION PROCESSING WITH MULTI-LEVEL ADAPTIVE DATA PROCESSING FLOW RATE CONTROL 有权

    公开(公告)号：US20230093511A1

    公开(公告)日：2023-03-23

    申请号：US17478135

    申请日：2021-09-17

    Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC

    Inventor： Shige WANG ,  Wei Tong ,  Shuqing Zeng

    IPC: G06F9/50 ,  G06F11/30 ,  G06F11/34 ,  G06F9/54

    Abstract: A perception processing system includes a memory and a main controller. The main controller includes modules and implements a data processing pipeline including algorithm stages, which are executed in parallel relative to sets of data and are executed sequentially relative to each of the sets of data. The algorithm stages share resources of the modules and the memory to process the sets of data and generate perception information. One of the modules executes global and local controllers. The global controller sets a processing rate for the local controllers. The local controllers monitor current processing rates of the algorithm stages. When one of the current processing rates is less than the set processing rate, the corresponding one of the local controllers sends a first signal to the global controller and in response the global controller sends a broadcast signal to the local controllers to adjust the current processing rates.

    Activation zero-bypass and weight pruning in neural networks for vehicle perception systems
    25.
    发明授权
    Activation zero-bypass and weight pruning in neural networks for vehicle perception systems 有权

    公开(公告)号：US11281974B2

    公开(公告)日：2022-03-22

    申请号：US16170698

    申请日：2018-10-25

    Applicant: GM GLOBAL TECHNOLOGY OPERATIONS LLC

    Inventor： Shuqing Zeng ,  Wei Tong ,  Shige Wang ,  Roman L. Millett

    IPC: B60R1/00 ,  G05D1/00 ,  G05D1/02 ,  G06N3/04 ,  G06N3/08

    Abstract: In one example implementation according to aspects of the present disclosure, a computer-implemented method includes capturing a plurality of images at a camera associated with a vehicle and storing image data associated with the plurality of images to a memory. The method further includes dispatching vehicle perception tasks to a plurality of processing elements of an accelerator in communication with the memory. The method further includes performing, by at least one of the plurality of processing elements, the vehicle perception tasks for the vehicle perception using a neural network, wherein performing the vehicle perception tasks comprises performing an activation bypass for values below a first threshold, and performing weight pruning of synapses and neurons of the neural network based at least in part on a second threshold. The method further includes controlling the vehicle based at least in part on a result of performing the vehicle perception tasks.

    Vehicle perception system on-line diangostics and prognostics
    26.
    发明授权
    Vehicle perception system on-line diangostics and prognostics 有权

    公开(公告)号：US10974730B2

    公开(公告)日：2021-04-13

    申请号：US16139782

    申请日：2018-09-24

    Applicant: GM Global Technology Operations LLC

    Inventor： Yao Hu ,  Wen-Chiao Lin ,  Wei Tong ,  Paul E. Krajewski

    IPC: B60W50/02 ,  G07C5/08 ,  G01C21/30 ,  G07C5/00

    Abstract: A method of on-line diagnostic and prognostic assessment of an autonomous vehicle perception system includes detecting, via a sensor, a physical parameter of an object external to the vehicle. The method also includes communicating data representing the physical parameter via the sensor to an electronic controller. The method additionally includes comparing the data from the sensor to data representing the physical parameter generated by a geo-source model. The method also includes comparing results generated by a perception software during analysis of the data from the sensor to labels representing the physical parameter from the geo-source model. Furthermore, the method includes generating a prognostic assessment of a ground truth for the physical parameter of the object using the comparisons of the sensor data to the geo-source model data and of the software results to the geo-source model labels. A system for on-line assessment of the vehicle perception system is also disclosed.

    Adversarial attack on black box object detection algorithm
    27.
    发明授权
    Adversarial attack on black box object detection algorithm 有权

    公开(公告)号：US10929719B2

    公开(公告)日：2021-02-23

    申请号：US16367590

    申请日：2019-03-28

    Applicant: GM Global Technology Operations LLC

    Inventor： Wei Tong ,  Shuqing Zeng ,  Upali P. Mudalige

    IPC: G06K9/00 ,  G06K9/62

    Abstract: Systems and methods to generate an adversarial attack on a black box object detection algorithm of a sensor involve obtaining an initial training data set from the black box object detection algorithm. The black box object detection algorithm performs object detection on initial input data to provide black box object detection algorithm output that provides the initial training data set. A substitute model is trained with the initial training data set such that output from the substitute model replicates the black box object detection algorithm output that makes up the initial training data set. Details of operation of the black box object detection algorithm are unknown and details of operation of the substitute model are known. The substitute model is used to perform the adversarial attack. The adversarial attack refers to identifying adversarial input data for which the black box object detection algorithm will fail to perform accurate detection.

    IDENTIFICATION OF ATTENTION REGION FOR ENHANCEMENT OF SENSOR-BASED DETECTION IN A VEHICLE
    28.
    发明申请
    IDENTIFICATION OF ATTENTION REGION FOR ENHANCEMENT OF SENSOR-BASED DETECTION IN A VEHICLE 审中-公开

    公开(公告)号：US20200311942A1

    公开(公告)日：2020-10-01

    申请号：US16367579

    申请日：2019-03-28

    Applicant: GM Global Technology Operations LLC

    Inventor： Wei Tong ,  Shuqing Zeng ,  Upali P. Mudalige

    IPC: G06T7/11 ,  B60R11/04 ,  G06T7/174 ,  G06K9/00

    Abstract: Systems and methods to identify an attention region in sensor-based detection involve obtaining a detection result that indicates one or more detection areas where one or more objects of interest are detected. The detection result is based on using a first detection algorithm. The method includes obtaining a reference detection result that indicates one or more reference detection areas where one or more objects of interest are detected. The reference detection result is based on using a second detection algorithm. The method also includes identifying the attention region as one of the one or more reference detection areas without a corresponding one or more detection areas. The first detection algorithm is used to perform detection in the attention region.

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