RCP-RF: A Comprehensive Road-car-pedestrian Risk Management Framework based on Driving Risk Potential Field
This work addresses the need for more effective risk management in automated vehicles to reduce traffic accidents, though it appears incremental by building on potential field theory with specific enhancements.
The authors tackled the problem of existing risk assessment frameworks for automated vehicles failing to handle complex traffic scenarios and ignoring motion tendencies by proposing RCP-RF, a comprehensive driving risk management framework based on potential field theory that integrates pedestrian risk and motion tendencies, achieving superior performance validated on real-world datasets and platforms.
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.