Baidu USA LLC

Baidu.com Times Technology (beijing) Co. Ltd.

Apollo Intelligent Driving Technology (beijing) Co., Ltd.

Baidu Online Network Technology (beijing) Co., Ltd.

Zhongpu Xia

Fan Zhu

Qi Kong

Yuchang Pan

Xiaoxin Fu

Hongye Li

Chunming Zhao

Guang Yu Yang

Jingao Wang

Qi Luo

Jinghao Miao

Liyun Li

Zhenguang Zhu

Xiang Yu

Sen Hu

Jiarui He

Haoyang Fan

Liangliang Zhang

Jiaming Tao

Xin Xu

Hui Jiang

Weicheng Zhu

Li Zhuang

Yifeng Pan

Feiyi Jiang

Yaqin Chen

Changchun Liu

Guang Yang

The present application provides a method and apparatus for planning an autonomous vehicle, an electronic device and a storage medium, which relates to the field of autonomous driving. According to the technical solutions of the present application, time points of entering a vehicle-converging area can be accurately predicted according to traveling conditions of two parties, so that a driving behavior of the autonomous vehicle is controlled more accurately. The specific implementation is as follows: acquiring first traveling information of an autonomous vehicle; acquiring second traveling information of at least one obstacle vehicle; predicting a driving intention of the obstacle vehicle according to the acquired second traveling information of the obstacle vehicle; planning a vehicle-converging strategy for the autonomous vehicle according to the first traveling information and the driving intention of the obstacle vehicle; and controlling the autonomous vehicle to travel according to the vehicle-converging strategy.

Method and apparatus for planning autonomous vehicle, electronic device and storage medium

According to one embodiment, when a predicted trajectory is received, a set of one or more features are extracted from at least some of the trajectory points of the predicted trajectory. The predicted trajectory is predicted using a prediction method or algorithm based on perception data perceiving an object within a driving environment surrounding an autonomous driving vehicle (ADV). The extracted features are fed into a predetermined DNN model to generate a similarity score. The similarity score represents a difference or similarity between the predicted trajectory and a prior actual trajectory that was used to train the DNN model. The similarity score can be utilized to evaluate the prediction method that predicted the predicted trajectory.

Evaluation framework for predicted trajectories in autonomous driving vehicle traffic prediction

In one embodiment, in response to perception data perceiving a driving environment surrounding an ADV, a map image of a map covering a location associated with the driving environment is obtained. An image recognition is performed on the map image to recognize one or more objects from the map image. An object may represent a particular road, a building structure (e.g., a parking lot, an intersection, or a roundabout). One or more features are extracted from the recognized objects, where the features may indicate or describe the traffic condition of the driving environment. Behaviors of one or more traffic participants perceived from the perception data are predicted based on the extracted features. A trajectory for controlling the ADV to navigate through the driving environment is planned based on the predicted behaviors of the traffic participants. A traffic participant can be a vehicle, a cyclist, or a pedestrian.

Traffic prediction based on map images for autonomous driving

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.

Map-less and localization-less lane following method for autonomous driving of autonomous driving vehicles on highway

According to an embodiment, a system generates a number of sample trajectories from a trajectory sample space for a driving scenario. The system determines a reward based on a reward model for each of the sample trajectories, where the reward model is generated using a rank based conditional inverse reinforcement learning algorithm. The system ranks the sample trajectories based on the determined rewards. The system determines a highest ranked trajectory based on the ranking. The system selects the highest ranked trajectory to control the ADV autonomously according to the highest ranked trajectory.

Auto-tuning motion planning system for autonomous vehicles

The present disclosure discloses a method and an apparatus for generating an autonomous driving strategy. A specific implementation of the method comprises: measuring state information of an instant vehicle and ambient scene information, the ambient scene information comprising: state information of an impediment vehicle, road structure information, and traffic scene information of the instant vehicle; determining a running track of the impediment vehicle according to the state information of the impediment vehicle within a predetermined period of time; determining a first mapping relation based on the state information of the impediment vehicle within the predetermined period of time and the running track of the impediment vehicle; and generating the autonomous driving strategy of the instant vehicle based on the first mapping relation, the road structure information, the state information of the instant vehicle and the traffic scene information of the instant vehicle.

Method and apparatus for generating automatic driving strategy

A lane departure detection system detects that an autonomous driving vehicle (ADV) is departing from the lane in which the ADV is driving based on sensor data captured when the ADV contact a deceleration curb such as a speed bump laid across the lane. When the ADV contacts the deceleration curb, the lane departure detection system detects and calculates an angle of a moving direction of the ADV vs a longitudinal direction of the deceleration curb. Based on the angle, the system calculates how much the moving direction of the ADV is off compared to a lane direction of the lane. The lane direction is typically substantially perpendicular to the longitudinal direction of the deceleration curb. A control command such as a speed control command and/or a steering control command is generated based on the angle to correct the moving direction of the ADV.

Deceleration curb-based direction checking and lane keeping system for autonomous driving vehicles

In one embodiment, when an ADV is driving on a road segment, a driving parameter is recorded in response to a first control command. A difference between the first driving parameter and a target driving parameter corresponding to the first control command is determined. In response to determining that the difference exceeds a predetermined threshold, a second control command is issued to compensate the difference and cause the ADV to drive with a second driving parameter closer to the target driving parameter. A slope status of the road segment is derived based on at least the second control command. Map data of a map corresponding to the road segment of the road is updated based on the derived slope status. The updated map can be utilized to generate and issue proper control commands in view of the slope status of the road when the ADV drives on the same road subsequently.

Method and system for updating maps based on control feedbacks of autonomous driving vehicles

In one embodiment, a lane departure detection system detects at a first point in time that a wheel of an ADV rolls onto a lane curb disposed on an edge of a lane in which the ADV is moving. The system detects at a second point in time that the wheel of the ADV rolls off the lane curb of the lane. The system calculates an angle between a moving direction of the ADV and a lane direction of the lane based on the time difference between the first point in time and the second point in time in view of a current speed of the ADV. The system then generates a control command based on the angle to adjust the moving direction of the ADV in order to prevent the ADV from further drifting off the lane direction of the lane.

Lane curb assisted off-lane checking and lane keeping system for autonomous driving vehicles

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Beijing, China

Zhongpu Xia