2023/05/04 by Shuhang Tan, Zhiling Wang, Tan, Shuhang +3 · 1 citation
Engineering · Mathematics · Psychology · #Artificial Intelligence (cs.AI) #Autonomous Vehicle Technology and Safety #Business #Computer science #Engineering #FOS: Computer and information sciences #FOS: Electrical engineering #Field (mathematics) #Geography #Human-Automation Interaction and Safety #Machine Learning (cs.LG) #Mathematics #Metric (unit) #Obstacle #Operations management #Pedestrian #Risk analysis (engineering) #Risk management #Systems and Control (eess.SY) #Traffic and Road Safety #Transport engineering #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2305.02493
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2023/05/04 · openalex created_date 2023/05/07 · openalex updated_date 2026/07/28
Recent years have witnessed the proliferation of traffic accidents, which led wide researches on Automated Vehicle (AV) technologies to reduce vehicle accidents, especially on risk assessment framework of AV technologies. However, existing time-based frameworks can not handle complex traffic scenarios and ignore the motion tendency influence of each moving objects on the risk distribution, leading to performance degradation. To address this problem, we novelly propose a comprehensive driving risk management framework named RCP-RF based on potential field theory under Connected and Automated Vehicles (CAV) environment, where the pedestrian risk metric are combined into a unified road-vehicle driving risk management framework. Different from existing algorithms, the motion tendency between ego and obstacle cars and the pedestrian factor are legitimately considered in the proposed framework, which can improve the performance of the driving risk model. Moreover, it requires only O(N 2) of time complexity in the proposed method. Empirical studies validate the superiority of our proposed framework against state-of-the-art methods on real-world dataset NGSIM and real AV platform.