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Adaptive velocity region-based path planning system for autonomous vehicle under multiple obstacles with various velocities

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Abstract

Path planning is the research hotspot of automatic driving technology. However, it is hard to ensure path safety for autonomous vehicle (AV) under multiple obstacles with various velocities. To solve this problem, an adaptive velocity region-based path planning system (AVR-PPS) which includes two parts, an adaptive velocity region controller (AVRC) and a path fusion controller (PFC), is presented in this work. The AVRC is designed to improve the velocity adaptivity through two units, a velocity region reconstruction unit (VRRU) and a potential field construction unit (PFCU). The VRRU restructures new velocity region to obtain the constraints of velocities, and the PFCU establishes an uniform potential field for the reconstructed velocity region to get the objective function for evaluating different velocities. The PFC is designed to generate the optimal path for obstacle avoidance using the constraints of multiple obstacles and the objective function derived from the AVRC. The path planning performance of the AVR-PPS is verified through simulation experiments on the MATLAB-CarSim simulation platform.

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Authors and Affiliations

  1. College of Electrical and Information Engineering, Hunan University, Changsha, Hunan, China

    Zhixian Liu & Xiaofang Yuan

  2. National Engineering Laboratory for Robot Vision Perception and Control, Hunan University, Changsha, Hunan, China

    Zhixian Liu & Xiaofang Yuan

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  1. Zhixian Liu
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  2. Xiaofang Yuan
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Correspondence to Zhixian Liu.

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Technical Editor: Adriano Almeida Gonçalves Siqueira.

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This work was supported in part by National Natural Science Foundation of China (No. 62073127), the Open Program of Hunan Provincial Key Laboratory of Vehicle Power and Transmission System (No.VPTS202002).

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Liu, Z., Yuan, X. Adaptive velocity region-based path planning system for autonomous vehicle under multiple obstacles with various velocities. J Braz. Soc. Mech. Sci. Eng. 44, 288 (2022). https://doi.org/10.1007/s40430-022-03597-6

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  • DOI: https://doi.org/10.1007/s40430-022-03597-6

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