Abstract: A method and a system for lane change prediction. The method includes collecting raw driving data, extracting a plurality of feature sets from the collected raw driving data, and obtaining corresponding lane change information. The lane change information indicates a status of lane change of a vehicle under each of the extracted feature sets. The method further includes automatically labeling each of the extracted plurality sets of features with the obtained corresponding lane change information. The method further includes training a lane change prediction model with the labeled plurality sets of features. Examples of the present disclosure further describe methods, systems, and vehicles for applying the lane change prediction model.

Abstract: A method for determining driving strategy of a vehicle includes obtaining localization information of the vehicle, a driving route of the vehicle, and a current driving velocity of the vehicle. The method further includes detecting and interpreting a current status of a traffic light. The method further includes recognizing a duration of the current status of the traffic light. The method further includes detecting a relevant stop line. The method further includes obtaining a distance between the stop line and the vehicle. The method further includes determining whether to change the current driving velocity according to the current status of the traffic light, the duration of the current status of the traffic light, the distance between the traffic stop line and the vehicle, and the current driving velocity. A data processing device for determining driving strategy of a vehicle is also provided.

Abstract: A method and a system for lane change prediction. The method includes collecting raw driving data, extracting a plurality of feature sets from the collected raw driving data, and obtaining corresponding lane change information. The lane change information indicates a status of lane change of a vehicle under each of the extracted feature sets. The method further includes automatically labeling each of the extracted plurality sets of features with the obtained corresponding lane change information. The method further includes training a lane change prediction model with the labeled plurality sets of features. Examples of the present disclosure further describe methods, systems, and vehicles for applying the lane change prediction model.

Abstract: A method for determining driving strategy of a vehicle includes obtaining localization information of the vehicle, a driving route of the vehicle, and a current driving velocity of the vehicle. The method further includes detecting and interpreting a current status of a traffic light. The method further includes recognizing a duration of the current status of the traffic light. The method further includes detecting a relevant stop line. The method further includes obtaining a distance between the stop line and the vehicle. The method further includes determining whether to change the current driving velocity according to the current status of the traffic light, the duration of the current status of the traffic light, the distance between the traffic stop line and the vehicle, and the current driving velocity. A data processing device for determining driving strategy of a vehicle is also provided.