RCP‐RF: A comprehensive road‐car‐pedestrian risk management framework based on driving risk potential field

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 cannot 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, a comprehensive driving risk management framework named RCP‐RF is novelly proposed based on potential field theory under connected and automated vehicles environment, where the pedestrian risk metric is 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(N2)$O(N^2)$ 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. A comprehensive driving risk management framework named RCP‐RF is novelly proposed based on potential field theory under connected and automated cehicles environment, where the pedestrian risk metric is 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.