CONDITIONAL VEHICLE TO EVERYTHING (V2X) SENSOR DATA SHARING

    公开(公告)号:US20230379673A1

    公开(公告)日:2023-11-23

    申请号:US17664159

    申请日:2022-05-19

    CPC classification number: H04W4/38 H04W4/40 G01S5/0072 H04W92/18

    Abstract: Certain aspects of the present disclosure provide techniques for techniques for sensor data sharing. Certain aspects provide a method for wireless communication by a first wireless device. The method generally includes receiving, from one or more sensors, corresponding one or more raw sensor data sets and transmitting, to a second wireless device, at least one message comprising information regarding an object detected based on processing the one or more raw sensor data sets, wherein the information includes one or more characteristics of the object derived based on processing the one or more raw sensor data sets, wherein: when one or more conditions are met, the information further includes at least one raw sensor data set of the one or more raw sensor data sets; and when the one or more conditions are not met, the information does not include any raw sensor data.

    MANAGING A DRIVING CONDITION ANOMALY

    公开(公告)号:US20220301423A1

    公开(公告)日:2022-09-22

    申请号:US17805638

    申请日:2022-06-06

    Abstract: Embodiments include methods performed by a processor of a vehicle control unit for managing a driving condition anomaly. In some embodiments, the vehicle may receive a first driving condition based on data from a first vehicle sensor, receive a second driving condition based on data from another data source, determine a driving condition anomaly based on the first driving condition and the second driving condition, send a request for information to a driving condition database remote from the vehicle, receive the requested information from the driving condition database, and resolve the driving condition anomaly based on the requested information from the driving condition database.

    MANAGING A DRIVING CONDITION ANOMALY

    公开(公告)号:US20220108604A1

    公开(公告)日:2022-04-07

    申请号:US17063269

    申请日:2020-10-05

    Abstract: Embodiments include methods performed by a processor of a vehicle control unit for managing a driving condition anomaly. In some embodiments, the vehicle may receive a first driving condition based on data from a first vehicle sensor, receive a second driving condition based on data from another data source, determine a driving condition anomaly based on the first driving condition and the second driving condition, send a request for information to a driving condition database remote from the vehicle, receive the requested information from the driving condition database, and resolve the driving condition anomaly based on the requested information from the driving condition database.

    V2X INFORMATION ELEMENTS FOR MANEUVER AND PATH PLANNING

    公开(公告)号:US20200326726A1

    公开(公告)日:2020-10-15

    申请号:US16816904

    申请日:2020-03-12

    Abstract: Techniques disclosed provide for enhanced V2X communications by defining information Elements (IE) for V2X messaging between V2X entities. For a transmitting vehicle that sends a V2X message to a receiving vehicle, these IEs are indicative of a detected vehicle model type detected by the transmitting vehicle of a detected vehicle; a pitch rate of the transmitting vehicle, a detected vehicle, or a detected object; a roll rate of the transmitting vehicle, a detected vehicle, or a detected object; a yaw rate of a detected vehicle, or a detected object; a pitch rate confidence; a roll rate confidence; an indication of whether a rear brake light of a detected vehicle is on; or an indication of whether a turning signal of a detected vehicle is on; or any combination thereof. With this information, the receiving vehicle is able to make more intelligent maneuvers than otherwise available through traditional V2X messaging.

    HYBRID REINFORCEMENT LEARNING FOR AUTONOMOUS DRIVING

    公开(公告)号:US20200150672A1

    公开(公告)日:2020-05-14

    申请号:US16683129

    申请日:2019-11-13

    Abstract: A method includes determining a current state of an environment of an autonomous agent, such as a vehicle. The method also includes determining, via a first neural network, a set of actions based on the current state. The method further includes determining whether further analysis of the set of actions is desired. The method selects an action from the set of actions using a model-based solution based on a reward and a risk of the action when further analysis is desired. The method also includes selecting the action from the set of actions according to a metric when further analysis is not desired. The method controls the autonomous agent to perform the selected action.

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