Abstract: In one embodiment, a lateral drifting error is determined based on at least a current location of an ADV. The lateral drifting error is segmented into a first drifting error and a second drifting error using a predetermined segmentation algorithm. A planning module plans a path or trajectory for a current driving cycle (e.g., planning cycle) to drive the ADV from the current location for a predetermined period of time. The planning module performs a first drifting error correction on the trajectory by modifying at least a starting point of the trajectory based on the first drifting error to generate a modified trajectory. A control module controls the ADV to drive according to the modified trajectory, including performing a second drifting error correction based on the second drifting error.

Abstract: A vehicle track prediction method and device, a storage medium and a terminal device are provided. The method includes: determining an obstacle vehicle entering a junction region in a case that an autonomous vehicle enters the junction region; acquiring historical traveling data of the obstacle vehicle in the junction region; predicting a potential track of the obstacle vehicle according to the historical traveling data and a current traveling state of the obstacle vehicle; and predicting a track of the autonomous vehicle in the junction region according to the potential track of the obstacle vehicle and a current traveling state of the autonomous vehicle. A decision making accuracy in self-driving may be effectively improved, and a driving risk may be reduced.

Abstract: In one embodiment, instead of using map data, a relative coordinate system is utilized to assist perception of the driving environment surrounding an ADV for some driving situations. One of such driving situations is driving on a highway. Typically, a highway has fewer intersections and exits. The relative coordinate system is utilized based on the relative lane configuration and relative obstacle information to control the ADV to simply follow the lane and avoid potential collision with any obstacles discovered within the road, without having to use map data. Once the relative lane configuration and obstacle information have been determined, regular path and speed planning and optimization can be performed to generate a trajectory to drive the ADV. Such a perception system is referred to as a relative perception system based on a relative coordinate system.

Abstract: In one embodiment, a lateral drifting error is determined based on at least a current location of an ADV. The lateral drifting error is segmented into a first drifting error and a second drifting error using a predetermined segmentation algorithm. A planning module plans a path or trajectory for a current driving cycle (e.g., planning cycle) to drive the ADV from the current location for a predetermined period of time. The planning module performs a first drifting error correction on the trajectory by modifying at least a starting point of the trajectory based on the first drifting error to generate a modified trajectory. A control module controls the ADV to drive according to the modified trajectory, including performing a second drifting error correction based on the second drifting error.

Abstract: A first localization system performs a first localization using a first set of sensors to track locations of the ADV along the path from a starting point to a destination point. A first localization curve is generated as a result representing the locations of the ADV along the path tracked by the first localization system. Currently, a second localization system performs a second localization using a second set of sensors to track the locations of the ADV along the path. A second localization curve is generated as a result representing the locations of the ADV along the path tracked by the second localization system. A system delay of the second localization system is determined by comparing the second localization curve against the first localization curve as a localization reference. The system delay of the second localization system can then be utilized to compensate path planning of the ADV subsequently.

Abstract: In one embodiment, instead of using map data, a relative coordinate system is utilized to assist perception of the driving environment surrounding an ADV for some driving situations. One of such driving situations is driving on a highway. Typically, a highway has fewer intersections and exits. The relative coordinate system is utilized based on the relative lane configuration and relative obstacle information to control the ADV to simply follow the lane and avoid potential collision with any obstacles discovered within the road, without having to use map data. Once the relative lane configuration and obstacle information have been determined, regular path and speed planning and optimization can be performed to generate a trajectory to drive the ADV. Such a perception system is referred to as a relative perception system based on a relative coordinate system.

Abstract: A vehicle track prediction method and device, a storage medium and a terminal device are provided. The method includes: determining an obstacle vehicle entering a junction region in a case that an autonomous vehicle enters the junction region; acquiring historical traveling data of the obstacle vehicle in the junction region; predicting a potential track of the obstacle vehicle according to the historical traveling data and a current traveling state of the obstacle vehicle; and predicting a track of the autonomous vehicle in the junction region according to the potential track of the obstacle vehicle and a current traveling state of the autonomous vehicle. A decision making accuracy in self-driving may be effectively improved, and a driving risk may be reduced.

Abstract: A track prediction method and device for an obstacle at a junction are provided. The method includes: acquiring environment information of a junction to be passed by a vehicle, and acquiring information on a visible obstacle in a sensible range of the vehicle, wherein the environment information comprises road information and information on a junction obstacle located in an area of the junction; combining the information on the junction obstacle with the information on the visible obstacle, and selecting information on a blind-zone obstacle in a blind zone of the vehicle at the junction; and predicting a moving track of an obstacle corresponding to the information on the blind-zone obstacle, according to the road information.

Abstract: A travelling track prediction method and device for a vehicle are provided. The method includes that: calculating a plurality of potential positions to which is to be reached by a target vehicle within a preset time within a sensible range of a main vehicle; selecting at least two positions from the plurality of potential positions as target positions; predicting a plurality of travelling tracks to each target position for the target vehicle based on travelling state information of the target vehicle; and selecting at least one travelling track from the plurality of travelling tracks as a prediction result of the target vehicle based on environment information around the target vehicle. According to the embodiments, the travelling track of the target vehicle around the main vehicle may be accurately predicted based on travelling state information and the environment information.

Abstract: A first localization system performs a first localization using a first set of sensors to track locations of the ADV along the path from a starting point to a destination point. A first localization curve is generated as a result representing the locations of the ADV along the path tracked by the first localization system. Currently, a second localization system performs a second localization using a second set of sensors to track the locations of the ADV along the path. A second localization curve is generated as a result representing the locations of the ADV along the path tracked by the second localization system. A system delay of the second localization system is determined by comparing the second localization curve against the first localization curve as a localization reference.