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Keywords = vehicle–pedestrian reaction space

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18 pages, 4027 KiB  
Article
A Collision Avoidance Strategy Based on Entropy-Increasing Risk Perception in a Vehicle–Pedestrian-Integrated Reaction Space
by Yongming Ding, Weiwei Zhang, Xuncheng Wu, Jiejie Xu and Jun Gong
World Electr. Veh. J. 2024, 15(5), 180; https://doi.org/10.3390/wevj15050180 - 24 Apr 2024
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Abstract
Ensuring pedestrian safety is one of the most significant challenges for autonomous driving systems in urban scenarios due to the non-cooperative and unpredictable nature of pedestrian movements. To tackle this problem, firstly, we propose a collision avoidance strategy based on entropy-increasing risk perception [...] Read more.
Ensuring pedestrian safety is one of the most significant challenges for autonomous driving systems in urban scenarios due to the non-cooperative and unpredictable nature of pedestrian movements. To tackle this problem, firstly, we propose a collision avoidance strategy based on entropy-increasing risk perception in a vehicle–pedestrian reaction space. Our approach combines a limited range of reaction space regions with entropy to quantify the risk of pedestrian–vehicle collision. Then, multi-vehicle candidate trajectories are generated using the path and speed sequence method, and the uncertain states of pedestrians are predicted based on the social force model and Markov model accordingly. Finally, to determine the optimal collision avoidance trajectory, we use quantitative reaction-space entropy as a new “cost function” to measure potential risk and perform multi-objective trajectory optimization based on the elitist non-dominated-sorting genetic algorithm region-focused (NSGA-RF) approach. Simulation results show that our proposed strategy can enhance the safety of the planned trajectory interaction between vehicles and pedestrians for autonomous driving under normal and emergency conditions. Full article
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