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- Title
A Collision Avoidance Strategy Based on Entropy-Increasing Risk Perception in a Vehicle–Pedestrian-Integrated Reaction Space.
- Authors
Ding, Yongming; Zhang, Weiwei; Wu, Xuncheng; Xu, Jiejie; Gong, Jun
- 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 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.
- Subjects
COST functions; TRAJECTORY optimization; PEDESTRIAN accidents; MARKOV processes; GENETIC algorithms; RISK perception
- Publication
World Electric Vehicle Journal, 2024, Vol 15, Issue 5, p180
- ISSN
2032-6653
- Publication type
Article
- DOI
10.3390/wevj15050180