MIXED MODE VEHICLE
    22.
    发明申请

    公开(公告)号:US20220185338A1

    公开(公告)日:2022-06-16

    申请号:US17119973

    申请日:2020-12-11

    Abstract: Systems and methods for mix mode automated driving including accessing a profile associated with an automated driving trip, determining a list of driving operations for the automated driving trip, and determining at least one recommended driving operation included in the list of operations based on the profile, such that the at least one recommended driving operation includes a first driving operation designated to autonomous control and a second driving operation designated to manual control.

    Method and apparatus for determining map improvements based on detected accidents

    公开(公告)号:US11341847B1

    公开(公告)日:2022-05-24

    申请号:US17110057

    申请日:2020-12-02

    Abstract: An approach is provided for determining map improvements based on detected accidents. The approach involves determining that a vehicle was involved in an accident. The approach also involves receiving a report indicating map data configured in the vehicle at a time of the accident. The approach further involves determining an association between the configured map data and the accident. The approach further involves performing one or more of the following operations: (1) transmitting the association and/or at least a portion of the configured map data to a database and/or a computing device; or (2) determining at least one map change to the configured map data based on the determined association, and transmitting the at least one map change to the vehicle, at least one other vehicle, the database, and/or the computing device.

    Apparatus and methods for predicting events in which drivers fail to see curbs

    公开(公告)号:US11854400B2

    公开(公告)日:2023-12-26

    申请号:US17735922

    申请日:2022-05-03

    CPC classification number: G08G1/165

    Abstract: An apparatus, method and computer program product are provided for predicting events in which drivers fail to see curbs while the drivers are maneuvering vehicles. In one example, the apparatus receives vehicle attribute data associated with a first vehicle, map data indicating one or more attributes of a road portion including a first curb, and sensor data indicating an orientation of a first driver within the first vehicle. The apparatus causes a machine learning model to render an output as a function of the vehicle attribute data, the map data, and the sensor data. The output indicates a likelihood of which the first driver will not be able to see the first curb at the road portion when the first driver is maneuvering the first vehicle. The machine learning model is trained to predict the output based on historical data indicating events in which second drivers maneuvered second vehicles to encounter the first curb or one or more second curbs.

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