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.

我们提出了一种名为RCP-RF的全面驾驶风险管理框架，基于潜在场理论在联网和自动驾驶汽车环境下，在该框架中，行人风险度量结合到统一的道路-车辆驾驶风险管理框架中。不同于现有算法，我们的框架合法地考虑了自我和障碍车之间的运动趋势和行人因素，可以提高驾驶风险模型的性能。此外，在所提出的方法中只需要O(N 2)的时间复杂度。经验研究验证了我们提出的框架在实际数据集NGSIM和真实AV平台上对最先进方法的优越性。

基于驾驶风险潜力场的综合道路车辆行人风险管理框架RCP-RF