SYSTEM AND PROCESS FOR CORRECTING GYROSCOPE DRIFT FOR A MOTOR VEHICLE

    公开(公告)号:US20230168088A1

    公开(公告)日:2023-06-01

    申请号:US17539700

    申请日:2021-12-01

    CPC classification number: G01C19/00 G01S13/931

    Abstract: A system of a motor vehicle includes a radar unit for generating a radar signal associated with a target positioned about the vehicle. The system further includes a tracker generating a tracker signal associated with a radar heading and a doppler based on the radar signal. The system further includes a wheel speed sensor for generating a wheel speed signal associated with a velocity of the vehicle. The system further includes a gyroscope for generating a gyro signal associated with a measured yaw rate. The system further includes a computer having a processor and a computer readable medium. The processor is programmed to determine a gyro drift and a corrected yaw rate while the vehicle is in motion, with the gyro drift and the corrected yaw rate being based on at least the radar heading, the doppler effect, and the velocity of the vehicle, and the measured yaw rate.

    Visual wireless based positioning exploiting multipath for outdoor positioning

    公开(公告)号:US12266187B2

    公开(公告)日:2025-04-01

    申请号:US17587699

    申请日:2022-01-28

    Abstract: An automobile vehicle visual wireless-based positioning system, incudes an automobile vehicle having a radio receiver. A map contains candidate locations of access-points (APs) and access-point corresponding media-access-control (MAC) IDs. A wireless range sensor determines different ranges of various detected APs visible to the automobile vehicle. An image collection feature identifies image data visible to the automobile vehicle. A real-time feature matching element matching features identified by image collection feature with data from the map. A filter receives an output from the real-time feature matching element to generate an automobile vehicle pose.

    SMART NOTIFICATIONS FOR VEHICLE PARKING GARAGE MANEUVERS

    公开(公告)号:US20240321106A1

    公开(公告)日:2024-09-26

    申请号:US18189414

    申请日:2023-03-24

    CPC classification number: G08G1/167 G08G1/0129 G01C21/206

    Abstract: A method can be used to provide smart notifications to avoid collisions while the vehicle maneuvers in a tight structural environment, such as a home garage or an underground parking lot. The method includes receiving historical vehicle-trajectory data. The historical vehicle-trajectory data includes the location and the heading of the vehicle for each of the plurality of historical trajectories along the structure. The method further includes clustering the plurality of historical trajectories of the vehicle along the structure by types of maneuvers to generate a plurality of trajectory clusters. The method also includes creating a probability distribution bitmap using the plurality of trajectory clusters and creating a topographic map based on the probability distribution bitmap.

    SCENE CREATION SYSTEM FOR AN AUTONOMOUS VEHICLE

    公开(公告)号:US20240246558A1

    公开(公告)日:2024-07-25

    申请号:US18157880

    申请日:2023-01-23

    Abstract: A scene creation system for an autonomous vehicle includes one or more controllers executing instructions to receive perception data and map data of a roadway the autonomous vehicle is traveling along, and identify a plurality of lane segments of the roadway that the autonomous vehicle travels along based on the perception data and the map data. The one or more controllers connect the plurality of lane segments together based on a spatial relationship between the plurality of lane segments to create a lane graph. The one or more controllers classify each of the plurality of lane segments of the lane graph to one or more lane attributes and reassemble the plurality of lane segments to create a representation of the roadway. The one or more controllers recreate a scene of an environment surrounding the autonomous vehicle based on the representation of the lanes that are part of the roadway.

    SYSTEM AND METHOD FOR EFFICIENT PLANNING UNDER UNCERTAINTY FOR AUTONOMOUS VEHICLES

    公开(公告)号:US20240092384A1

    公开(公告)日:2024-03-21

    申请号:US17933587

    申请日:2022-09-20

    CPC classification number: B60W60/0011 G05D1/0088 G05D1/0212 G05D2201/0213

    Abstract: A method for efficient autonomous driving planning includes receiving a current driving-scene data and a predicted driving-scene data. The current driving-scene data is indicative of a current driving scene around a host vehicle. The predicted driving-scene data is indicative of a predicted driving scene around the host vehicle. The predicted driving scene around the host vehicle is different from the current driving scene around the host vehicle. The method further includes converting the current driving-scene data and the predicted driving-scene data into a first scene-graph and a second scene-graph, respectively. The method further includes determining a plurality of scene change metrics using the first scene-graph and the second scene-graph. The method further includes selecting between a first trajectory planning process and a second trajectory planning process based on the plurality of scene change metrics.

    SYSTEM AND METHOD FOR ESTIMATING CLOUD COVER FROM A MOVING VEHICLE

    公开(公告)号:US20240077644A1

    公开(公告)日:2024-03-07

    申请号:US17929146

    申请日:2022-09-01

    Abstract: A system for estimating cloud cover from moving vehicles includes: vehicle mounted sensors communicating with one or more control modules. The control modules have processors, memory, and input/output (I/O) ports. The I/O ports of the control modules of the vehicles communicate with the sensors. The control modules execute program code stored in the memory including first and second algorithm portions. The first algorithm portion, collects sensor data from the sensors, estimates atmospheric parameters from the sensor data; and computes measurement coordinates from the sensor data and atmospheric parameters. The second algorithm portion generates and selectively updates a cloud map based on from data received from the first algorithm portion, and from high-definition map (HD Map) data.

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