RCP-RF：基于驾驶风险势场的综合道路-车辆-行人风险管理框架
RCP-RF: A Comprehensive Road-car-pedestrian Risk Management Framework based on Driving Risk Potential Field
AI总结：
针对现有自动驾驶风险评估框架的不足，提出基于势场理论的RCP-RF综合框架，纳入车辆运动趋势与行人因素，时间复杂度O(N²)，在真实数据集和平台上性能优于现有最优方法。
摘要：
近年来交通事故频发，推动了自动驾驶车辆（AV）相关技术的广泛研究以减少车辆事故，其中重点之一是AV技术的风险评估框架。然而，现有基于时间的框架无法应对复杂交通场景，且忽略了各运动物体的运动趋势对风险分布的影响，导致性能下降。为解决该问题，我们在网联自动驾驶车辆（CAV）环境下，基于势场理论创新性提出了名为RCP-RF的综合驾驶风险管理框架，将行人风险指标整合进统一的道路-车辆驾驶风险管理框架中。与现有算法不同，该框架合理纳入了自车与障碍车辆间的运动趋势以及行人因素，可提升驾驶风险模型的性能，且该方法的时间复杂度仅为O(N²)。实证研究在真实世界数据集NGSIM和真实AV平台上验证了所提框架相较于现有最优方法的优越性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
