Activation zero-bypass and weight pruning in neural networks for vehicle perception systems

    公开(公告)号:US11281974B2

    公开(公告)日:2022-03-22

    申请号:US16170698

    申请日:2018-10-25

    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.

    System and method for radar cross traffic tracking and maneuver risk estimation

    公开(公告)号:US11155258B2

    公开(公告)日:2021-10-26

    申请号:US16362889

    申请日:2019-03-25

    Abstract: A risk maneuver assessment system and method to generate a perception of an environment of a vehicle and a behavior decision making model for the vehicle; a sensor system configured to provide the sensor input in the environment for filtering target objects; one or more modules configured to map and track target objects to make a candidate detection from multiple candidate detections of a true candidate detection as the tracked target object; apply a Markov Random Field (MRF) algorithm for recognizing a current situation of the vehicle and predict a risk of executing a planned vehicle maneuver at the true detection of the dynamically tracked target; apply mapping functions to sensed data of the environment for configuring a machine learning model of decision making behavior of the vehicle; and apply adaptive threshold to cells of an occupancy grid for representing an area of tracking of objects within the vehicle environment.

    Camera vision systems
    83.
    发明授权

    公开(公告)号:US10965857B1

    公开(公告)日:2021-03-30

    申请号:US16737057

    申请日:2020-01-08

    Abstract: In various embodiments, cameras and mobile platforms are provided. In one exemplary embodiment, a mobile platform is provided that includes a body and a camera disposed on the body. The camera includes one or more image sensors, and one or more lens assemblies. The one or more lens assemblies are configured with respect to the one or more image sensors, that at least one image plane from the one or more image sensors is tilted to form a non-zero angle with at least one equivalent lens plane from the one or more lens assemblies, transferring the image focal plane to be parallel to the movement direction of the mobile platform in which the camera is installed. The use of multiple image sensors or lens assemblies in certain embodiments increases camera angle of view.

    Adversarial attack on black box object detection algorithm

    公开(公告)号:US10929719B2

    公开(公告)日:2021-02-23

    申请号:US16367590

    申请日:2019-03-28

    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

    公开(公告)号:US20200311942A1

    公开(公告)日:2020-10-01

    申请号:US16367579

    申请日:2019-03-28

    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.

    RESOLUTION OF DOPPLER AMBIGUITY IN A RADAR SYSTEM THROUGH TRACKING

    公开(公告)号:US20200264274A1

    公开(公告)日:2020-08-20

    申请号:US16279286

    申请日:2019-02-19

    Abstract: A system and method to resolve ambiguity in a radar system involve detecting one or more objects with the radar system. The detecting includes obtaining range, azimuth, and an ambiguous range rate of a first object of the one or more objects. A plurality of Kalman filters are generated with state variables that include parameters based on the range, the azimuth, and the ambiguous range rate. Each of the plurality of Kalman filters provides a different estimate for an unambiguous range rate. The method also includes updating the plurality of Kalman filters using additional detections by the radar system, selecting a selected Kalman filter from among the plurality of Kalman filters that exhibits a highest probability mass among a plurality of probability mass corresponding with and derived from the plurality of Kalman filters, and determining the unambiguous range rate of the object using the selected Kalman filter.

    Control systems, control methods and controllers for an autonomous vehicle

    公开(公告)号:US10678253B2

    公开(公告)日:2020-06-09

    申请号:US15988658

    申请日:2018-05-24

    Abstract: Systems and methods are provided for controlling an autonomous vehicle (AV). A map generator module processes sensor data to generate a world representation of a particular driving scenario (PDS). A scene understanding module (SUM) processes navigation route data, position information and a feature map to define an autonomous driving task (ADT), and decomposes the ADT into a sequence of sub-tasks. The SUM selects a particular combination of sensorimotor primitive modules (SPMs) to be enabled and executed for the PDS. Each one of the SPMs addresses a sub-task in the sequence. A primitive processor module executes the particular combination of the SPMs such that each generates a vehicle trajectory and speed (VTS) profile. A selected one of the VTS profiles is then processed to generate the control signals, which are then processed at a low-level controller to generate commands that control one or more of actuators of the AV.

    AUTO-CALIBRATION FOR VEHICLE CAMERAS
    90.
    发明申请

    公开(公告)号:US20200027241A1

    公开(公告)日:2020-01-23

    申请号:US16039640

    申请日:2018-07-19

    Abstract: In various embodiments, methods, systems, and vehicles are provided for calibrating vehicle cameras. In certain embodiments, a vehicle includes a camera, a memory, and a processor. The camera is disposed onboard the vehicle, and is configured to generate a camera image in which an object is detected onboard the vehicle. The memory is configured to store map data relating to the detected object. The processor is disposed onboard the vehicle, and is configured to perform an initial projection using the map data and initial values of calibration parameters for the camera; and update values of the calibration parameters using a comparison of the initial projection with the camera image.

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