PERMISSIONS FOR PARTIALLY AUTONOMOUS VEHICLE OPERATION

    公开(公告)号:US20180059663A1

    公开(公告)日:2018-03-01

    申请号:US15254052

    申请日:2016-09-01

    IPC分类号: G05D1/00 G06K9/00

    摘要: A vehicle system includes an autonomous mode controller and a processor. The autonomous mode controller is programmed to control a host vehicle in a partially autonomous mode. The processor is programmed to identify a driver, determine whether the driver is authorized to operate the host vehicle in the partially autonomous mode, and disable the partially autonomous mode if the driver is not authorized to operate the host vehicle in the partially autonomous mode.

    Autonomous control in a dense vehicle environment
    9.
    发明授权
    Autonomous control in a dense vehicle environment 有权
    密集车辆环境中的自主控制

    公开(公告)号:US09079587B1

    公开(公告)日:2015-07-14

    申请号:US14180788

    申请日:2014-02-14

    摘要: A computer in a first vehicle is programmed to receive a first set of data from at least one sensor in the first vehicle and to receive a second set of data from at least one second vehicle. The second set of data is from at least one sensor in the at least one second vehicle. The computer is further programmed to use both the first set of data and the second set of data to identify at least one feature of a road being traversed by the first vehicle.

    摘要翻译: 第一车辆中的计算机被编程为从第一车辆中的至少一个传感器接收第一组数据,并从至少一个第二车辆接收第二组数据。 第二组数据来自至少一个第二车辆中的至少一个传感器。 计算机还被编程为同时使用第一组数据和第二组数据来识别由第一车辆穿过的道路的至少一个特征。

    Vehicle operation labeling
    10.
    发明授权

    公开(公告)号:US11429843B2

    公开(公告)日:2022-08-30

    申请号:US16683912

    申请日:2019-11-14

    摘要: A computer, including a processor and a memory, the memory including instructions to be executed by the processor to identify patterns in first high anticipation scenarios based on user identification, wherein high anticipation scenarios include video sequences wherein a first vehicle will be within a specified distance of a first object in a first environment around the first vehicle, wherein user identification is determined by viewing portions of a respective video sequence. A first model including a first deep neural network can be trained to determine second high anticipation scenarios based on the patterns identified in the first high anticipation scenarios and a second model including a second deep neural network can be trained to modify locations and velocities of second objects in the first high anticipation scenarios and output modified high anticipation scenarios. The computer can be further programmed to train a third model including a third deep neural network to operate a vehicle based on the modified high anticipation scenarios output by the second model.