Vehicle Turn Signal Detection
    12.
    发明申请

    公开(公告)号:US20170174261A1

    公开(公告)日:2017-06-22

    申请号:US14973454

    申请日:2015-12-17

    Abstract: Systems, methods, and devices for detecting a vehicle's turn signal status for collision avoidance during lane-switching maneuvers or otherwise. A method includes detecting, at a first vehicle, a presence of a second vehicle in an adjacent lane. The method includes identifying, in an image of the second vehicle, a sub-portion containing a turn signal indicator of the second vehicle. The method includes processing the sub-portion of the image to determine a state of the turn signal indicator. The method also includes notifying a driver or performing a driving maneuver, at the first vehicle, based on the state of the turn signal indicator.

    INTELLIGENT VEHICLE ACCESS POINT OPENING SYSTEM

    公开(公告)号:US20170114583A1

    公开(公告)日:2017-04-27

    申请号:US14923038

    申请日:2015-10-26

    CPC classification number: E05F15/40 E05F15/73 E05Y2900/531

    Abstract: Methods and apparatus pertaining to an intelligent vehicle access point opening system are provided. A method may involve detecting a presence of an object in a vicinity of a cover of an access point of a vehicle. The method may also involve receiving a command to open the cover and activating a mechanism to open the cover responsive to receiving the command. The method may further involve determining whether the object is likely to fall as the cover is being opened. The method may additionally involve pausing opening of the cover responsive to a determination that the object is likely to fall as the cover is being opened.

    Vehicle turn signal detection
    17.
    发明授权

    公开(公告)号:US10800455B2

    公开(公告)日:2020-10-13

    申请号:US14973454

    申请日:2015-12-17

    Abstract: Systems, methods, and devices for detecting a vehicle's turn signal status for collision avoidance during lane-switching maneuvers or otherwise. A method includes detecting, at a first vehicle, a presence of a second vehicle in an adjacent lane. The method includes identifying, in an image of the second vehicle, a sub-portion containing a turn signal indicator of the second vehicle. The method includes processing the sub-portion of the image to determine a state of the turn signal indicator. The method also includes notifying a driver or performing a driving maneuver, at the first vehicle, based on the state of the turn signal indicator.

    Parking-lot-navigation system and method

    公开(公告)号:US10481609B2

    公开(公告)日:2019-11-19

    申请号:US15373845

    申请日:2016-12-09

    Abstract: A system and method for assisted or autonomous parking of a vehicle is disclosed. The method may begin when the vehicle approaches a feeder lane within a parking lot. At that point, a computer system may decide whether the vehicle should enter the feeder lane. The computer system may use at least one of machine learning, computer vision, and range measurements to determining whether a condition precedent for entering the feeder lane exists. The condition precedent may include an in-bound arrow on the feeder lane or parking lines and/or a parked vehicle adjacent the feeder lane defining a departure angle less than or equal to ninety degrees. If the condition precedent exists, the vehicle may enter the feeder lane. If the condition precedent does not exist, the vehicle may move on to another feeder lane.

    Training algorithm for collision avoidance

    公开(公告)号:US10474964B2

    公开(公告)日:2019-11-12

    申请号:US15007024

    申请日:2016-01-26

    Abstract: A machine learning model is trained by defining a scenario including models of vehicles and a typical driving environment. A model of a subject vehicle is added to the scenario and sensor locations are defined on the subject vehicle. A perception of the scenario by sensors at the sensor locations is simulated. The scenario further includes a model of a lane-splitting vehicle. The location of the lane-splitting vehicle and the simulated outputs of the sensors perceiving the scenario are input to a machine learning algorithm that trains a model to detect the location of a lane-splitting vehicle based on the sensor outputs. A vehicle controller then incorporates the machine learning model and estimates the presence and/or location of a lane-splitting vehicle based on actual sensor outputs input to the machine learning model.

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