Expressway Lane Changing Trajectory Planning Method of Autonomous Vehicle Based on Dynamic Movement Primitives

doi:10.3901/JME.2024.10.192

Abstract

Abstract: To enhance drivers’ acceptance of autonomous vehicles while also addressing the decoupling path and velocity in trajectory planning, an expressway lane-changing trajectory planning method based on dynamic movement primitives(DMPs) is proposed utilizing natural expressway driving data. First, the expressway’s historical lane change trajectory is analyzed and processed using the maximum likelihood estimation to obtain the teaching lane change trajectory, which can characterize the natural driver’s lane change characteristics. Second, the DMPs algorithm is adopted to learn the teaching lane change trajectory, complete the trajectory replication, and construct a library of movement primitives. Then, based on the library of movement primitives and lane change requirements, multiple lane change trajectories can be generalized that can suit expressway driving scenarios. Finally, the planned trajectory’s real-time and traceability is verified based on the joint Prescan/Matlab/Carsim simulation and vehicle experiment. The results demonstrate that the proposed DMPs algorithm offers efficient computation, excellent generalization capability, and high acceptance by drivers. Moreover, it enables cooperative planning of lane change path and velocity.

Key words: autonomous vehicle, lane changing trajectory planning, dynamic movement primitives, teaching trajectory

CLC Number:

U461

LIANG Kaichong, ZHAO Zhiguo, YAN Danshu, ZHAO Kun. Expressway Lane Changing Trajectory Planning Method of Autonomous Vehicle Based on Dynamic Movement Primitives[J]. Journal of Mechanical Engineering, 2024, 60(10): 192-206.

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