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.

翻译：近年来，交通事故频发推动了自动驾驶汽车技术研究以减少车辆事故，尤其是针对自动驾驶技术风险评估框架的发展。然而，现有的基于时间的框架难以处理复杂交通场景，且忽略了各移动物体运动趋势对风险分布的影响，导致性能下降。为解决该问题，我们创新性地提出了一种基于势场理论的全面行车风险管控框架RCP-RF，该框架在车联网环境下将行人风险度量统一整合至道路-车辆行车风险管理框架中。与现有算法不同，所提框架合理考虑了自车与障碍车辆间的运动趋势及行人因素，从而提升行车风险模型性能。此外，本方法仅需O(N²)的时间复杂度。基于现实数据集NGSIM与真实自动驾驶平台的实证研究验证了该框架相较于现有先进方法的优越性。