Motion Planning Algorithm of Autonomous Driving Considering Interactive Trajectory Prediction

doi:10.12068/j.issn.1005-3026.2022.07.003

Abstract

Abstract: Aiming at the problem of predicting the surrounding traffic situation in autonomous vehicle motion planning， a motion planning algorithm considering the interactive trajectory prediction between surrounding vehicles is proposed. Firstly， for structured road information， an improved social force model is constructed to predict the trajectory of vehicles around autonomous vehicles. Secondly， the predicted trajectory and the trajectory set that is generated in the Frenet coordinate system are projected on the space-time occupancy map， and the shortest distance between the projection points is calculated for collision checking. To obtain candidate trajectories， the trajectories are selected by collision， acceleration and curvature checking. Then， the cost function is constructed to evaluate the candidate trajectories and obtain the optimal motion trajectory. Finally， the simulation results in different driving scenarios show that the motion planning algorithm can make decisions about driving behavior in advance. The planned speed curve is more stable and the safety， comfort and driving efficiency of the motion trajectory are better.

Key words: autonomous driving; motion planning; interactive trajectory prediction; social force model; cost function

CLC Number:

TP391.4

LIU Qi-ran， LIAN Jing， CHEN Shi， FAN Rong. Motion Planning Algorithm of Autonomous Driving Considering Interactive Trajectory Prediction[J]. Journal of Northeastern University(Natural Science), 2022, 43(7): 930-936.

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