Remote safe driving methods and systems

    公开(公告)号:US11292480B2

    公开(公告)日:2022-04-05

    申请号:US16569640

    申请日:2019-09-12

    Applicant: TuSimple, Inc.

    Abstract: Disclosed are devices, systems and methods for remote safe driving. One exemplary method includes detecting an emergency situation, and in response to the detecting the emergency situation, switching operation of the vehicle to a low-power operation mode that comprises shutting down a subset of vehicular components, and periodically transmitting a location of the vehicle to a remote monitoring center. Another exemplary method includes selecting at least one of a set of vehicular driving actions, and transmitting, over a secure connection, the at least one of the set of vehicular driving actions to the vehicle, wherein the set of vehicular driving actions is generated based on a classification of driver behavior.

    Output of a neural network method for deep odometry assisted by static scene optical flow

    公开(公告)号:US10552979B2

    公开(公告)日:2020-02-04

    申请号:US15703885

    申请日:2017-09-13

    Applicant: TUSIMPLE

    Abstract: A method of visual odometry for a non-transitory computer readable storage medium storing one or more programs is disclosed. The one or more programs includes instructions, which when executed by a computing device, causes the computing device to perform the following steps comprising: performing data alignment among sensors including a LiDAR, cameras and an IMU-GPS module; collecting image data and generating point clouds; processing, in the IMU-GPS module, a pair of consecutive images in the image data to recognize pixels corresponding to a same point in the point clouds; and establishing an optical flow for visual odometry.

    Neural network architecture system for deep odometry assisted by static scene optical flow

    公开(公告)号:US10671083B2

    公开(公告)日:2020-06-02

    申请号:US15703879

    申请日:2017-09-13

    Applicant: TuSimple, Inc.

    Abstract: A system for visual odometry is disclosed. The system includes: an internet server, comprising: an I/O port, configured to transmit and receive electrical signals to and from a client device; a memory; one or more processing units; and one or more programs stored in the memory and configured for execution by the one or more processing units, the one or more programs including instructions for: extracting representative features from a pair input images in a first convolution neural network (CNN) in a visual odometry model; merging, in a first merge module, outputs from the first CNN; decreasing feature map size in a second CNN; generating a first flow output for each layer in a first deconvolution neural network (DNN); merging, in a second merge module, outputs from the second CNN and the first DNN; generating a second flow output for each layer in a second DNN; and reducing accumulated errors in a recurrent neural network (RNN).

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