Volume 23 Issue 5
Oct.  2023
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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2023  >  23(5): 264-282
WANG Shun-chao, LI Zhi-bin, CAO Qi, WANG Bing-tong, DING Hong-liang. Reciprocal velocity obstacle algorithm for collision risk avoidance of intelligent connected vehicles[J]. Journal of Traffic and Transportation Engineering, 2023, 23(5): 264-282. doi: 10.19818/j.cnki.1671-1637.2023.05.019
Citation: WANG Shun-chao, LI Zhi-bin, CAO Qi, WANG Bing-tong, DING Hong-liang. Reciprocal velocity obstacle algorithm for collision risk avoidance of intelligent connected vehicles[J]. Journal of Traffic and Transportation Engineering, 2023, 23(5): 264-282. doi: 10.19818/j.cnki.1671-1637.2023.05.019
shu

Reciprocal velocity obstacle algorithm for collision risk avoidance of intelligent connected vehicles

doi: 10.19818/j.cnki.1671-1637.2023.05.019
  • 1.

    School of Transportation, Southeast University, Nanjing 211189, Jiangsu, China

  • 2.

    Institute of Smart City and Intelligent Transportation, Southwest Jiaotong University, Chengdu 611730, Sichuan, China

Funds:

National Natural Science Foundation of China 52272331

National Natural Science Foundation of China 52232012

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    Abstract
  • A reciprocal velocity obstacle (RVO) algorithm for collision risk detection and collaborative path planning for collision avoidance of intelligent connected vehicles was constructed to address the dynamic collision avoidance in the collaborative driving among multiple intelligent vehicles. Based on the artificial potential field (APF) theory, a vehicle collision risk potential field (CRPF) was built to quantify both the collision risk intensity and risk area. According to the interactive effect of vehicle driving behavior, an RVO algorithm was constructed to determine the conditions and rules for collaborative collision risk avoidance among conflicting vehicles. Based on the vehicle dynamics constraints, a dynamic window approach was established to identify the feasible velocity solution set for collision risk avoidance. Based on the principle of model predictive control, the optimization theory was employed to build a path planning model for the vehicle collision risk avoidance. The effectiveness of the proposed collision risk avoidance algorithm was tested and compared with other collision avoidance algorithms by constructing the collision avoidance simulation scenarios for the single conflicting vehicle, multiple conflicting vehicles, and conflicting traffic flow in bottleneck areas under an intelligent connected environment. Research results show that compared to other comparative algorithms, the security performance and efficiency performance of the RVO algorithm improves by more than 8.6% and 9.6%, respectively, indicating that the proposed RVO algorithm can effectively reduce the collision avoidance velocity and trajectory fluctuations for conflicting vehicles via the collaborative collision avoidance behavior, effectively avoid the collision conflicts among vehicles with nonlinear speeds and trajectories and mitigates the multiple vehicle collisions and significant traffic flow fluctuations in bottleneck areas. The proposed collision avoidance algorithm outperforms other algorithms in bottleneck areas with large-scale vehicle conflicts, enhancing the vehicle traffic efficiency by 10.42% and reducing the vehicle collision risk by 47.32%. Thus, the algorithm has sound performance in coordinating the collision avoidance behavior of large-scale conflict vehicles and reducing the vehicle collision risks and operation delays. 2 tabs, 20 figs, 41 refs.

     

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