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    MULTI-SENSOR COLLABORATIVE CALIBRATION SYSTEM
    13.
    发明公开
    MULTI-SENSOR COLLABORATIVE CALIBRATION SYSTEM 审中-公开

    公开(公告)号：US20240241515A1

    公开(公告)日：2024-07-18

    申请号：US18621904

    申请日：2024-03-29

    申请人： TUSIMPLE, INC.

    发明人： Chenzhe QIAN ,  Chenghao GONG ,  Fuheng DENG

    IPC分类号： G05D1/00 ,  G01C25/00 ,  G01S7/497 ,  G01S13/86 ,  G01S17/931

    CPC分类号： G05D1/0011 ,  G01C25/005 ,  G01S7/497 ,  G01S13/865 ,  G01S17/931

    摘要： Technique for performing multi-sensor collaborative calibration on a vehicle is disclosed. A method includes obtaining, from at least two sensors located on a vehicle, sensor data items of an area that comprises a plurality of calibration objects; determining, from the sensor data items, attributes of the plurality of calibration objects; determining, for the at least two sensors, an initial matrix that describes a first set of extrinsic parameters between the at least two sensors based at least on the attributes of the plurality of calibration objects; determining an updated matrix that describes a second set of extrinsic parameters between the at least two sensors based at least on the initial matrix and a location of at least one calibration object; and performing autonomous operation of the vehicle using the second set of extrinsic parameters and additional sensor data received from the at least two sensors.

    SYSTEM AND METHOD FOR SENSOR CLEANING AND POSITIONING
    14.
    发明公开
    SYSTEM AND METHOD FOR SENSOR CLEANING AND POSITIONING 审中-公开

    公开(公告)号：US20240239306A1

    公开(公告)日：2024-07-18

    申请号：US18410628

    申请日：2024-01-11

    申请人： TuSimple, Inc.

    发明人： Juexiao NING

    IPC分类号： B60S1/56 ,  B60Q1/50

    CPC分类号： B60S1/56 ,  B60Q1/507 ,  G01S7/4043

    摘要： Provided herein is a system and method for sensor cleaning and positioning of the sensor on a vehicle. A system can include a mount attached to a vehicle; a sensor base; a sensor supported on the sensor base; a boom connecting the mount to the sensor base; and a tether, where the tether is connected at a first end, at least indirectly, to the sensor, and at a second end to a structure within the vehicle. The system may include a sensor cleaning array of nozzles, where combined spray patterns of the array of nozzles cover a sensor window of the sensor about a field-of-view of the sensor.

    MICROPHONE ARRAYS TO OPTIMIZE THE ACOUSTIC PERCEPTION OF AUTONOMOUS VEHICLES
    15.
    发明公开
    MICROPHONE ARRAYS TO OPTIMIZE THE ACOUSTIC PERCEPTION OF AUTONOMOUS VEHICLES 审中-公开

    公开(公告)号：US20240196124A1

    公开(公告)日：2024-06-13

    申请号：US18517517

    申请日：2023-11-22

    申请人： TuSimple, Inc.

    发明人： Pengji Duan ,  Yusheng Ding ,  Qiwei Li

    IPC分类号： H04R1/08 ,  B60W10/18 ,  B60W10/20

    CPC分类号： H04R1/083 ,  B60W10/18 ,  B60W10/20 ,  B60W2420/54 ,  B60W2510/18 ,  B60W2510/20 ,  H04R2201/40 ,  H04R2499/13

    摘要： A system comprises a microphone array and a processor. The microphone array detects first sound signal and the second sound signal. Each of the first and second sound signals has a particular frequency band. Each of the first and second sound signals is originated from a particular sound source. The processor receives the first and second sound signals. The processor amplifies the first sound signals, each with a different amplification order. The processor disregards the second sound signal, where the second sound signal includes interference noise signals. The processor determines that the first sound signals indicate that a vehicle is within a threshold distance from an autonomous vehicle and traveling in a direction toward the autonomous vehicle. In response, the processor instructs the autonomous vehicle to perform a minimal risk condition operation. The minimal risk condition operation includes pulling over or stopping the autonomous vehicle.

    Neural network based vehicle dynamics model
    16.
    发明授权
    Neural network based vehicle dynamics model 有权

    公开(公告)号：US12007778B2

    公开(公告)日：2024-06-11

    申请号：US18094363

    申请日：2023-01-08

    申请人： TuSimple, Inc.

    发明人： Jun Chen ,  Wutu Lin ,  Liu Liu

    IPC分类号： G06F30/20 ,  G05D1/00 ,  G06N3/00

    CPC分类号： G05D1/0221 ,  G05D1/0088 ,  G05D1/0274 ,  G05D1/0285 ,  G06N3/00

    摘要： A system and method for implementing a neural network based vehicle dynamics model are disclosed. A particular embodiment includes: training a machine learning system with a training dataset corresponding to a desired autonomous vehicle simulation environment; receiving vehicle control command data and vehicle status data, the vehicle control command data not including vehicle component types or characteristics of a specific vehicle; by use of the trained machine learning system, the vehicle control command data, and vehicle status data, generating simulated vehicle dynamics data including predicted vehicle acceleration data; providing the simulated vehicle dynamics data to an autonomous vehicle simulation system implementing the autonomous vehicle simulation environment; and using data produced by the autonomous vehicle simulation system to modify the vehicle status data for a subsequent iteration.

    PREDICTION-BASED SYSTEM AND METHOD FOR TRAJECTORY PLANNING OF AUTONOMOUS VEHICLES
    18.
    发明公开
    PREDICTION-BASED SYSTEM AND METHOD FOR TRAJECTORY PLANNING OF AUTONOMOUS VEHICLES 审中-公开

    公开(公告)号：US20240168478A1

    公开(公告)日：2024-05-23

    申请号：US18431194

    申请日：2024-02-02

    申请人： TuSimple, Inc.

    发明人： Xiaomin ZHANG ,  Yilun CHEN ,  Guangyu LI ,  Xing SUN ,  Wutu LIN ,  Liu LIU ,  Kai-Chieh MA ,  Zijie XUAN ,  Yufei ZHAO

    IPC分类号： G05D1/00 ,  G05D1/228 ,  G05D1/646 ,  G08G1/16

    CPC分类号： G05D1/0212 ,  G05D1/228 ,  G05D1/646 ,  G08G1/161 ,  G08G1/166 ,  G08G1/167

    摘要： A prediction-based system and method for trajectory planning of autonomous vehicles is configured to: receive data from a training data collection system, the training data including perception data and context data corresponding to human driving behaviors; perform a training phase for training a trajectory prediction module using the training data; receive perception data associated with a host vehicle; and perform an operational phase for extracting host vehicle feature data and proximate vehicle context data from the perception data, generating a proposed trajectory for the host vehicle, using the trained trajectory prediction module to generate predicted trajectories for each of one or more proximate vehicles near the host vehicle based on the proposed host vehicle trajectory, determining if the proposed trajectory for the host vehicle will conflict with any of the predicted trajectories of the proximate vehicles, and modifying the proposed trajectory for the host vehicle until conflicts are eliminated.