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Recent years have witnessed the proliferation of traffic accidents, which ledwide researches on Automated Vehicle (AV) technologies to reduce vehicleaccidents, especially on risk assessment framework of AV technologies. However,existing time-based frameworks can not handle complex traffic scenarios andignore the motion tendency influence of each moving objects on the riskdistribution, leading to performance degradation. To address this problem, wenovelly propose a comprehensive driving risk management framework named RCP-RFbased on potential field theory under Connected and Automated Vehicles (CAV)environment, where the pedestrian risk metric are combined into a unifiedroad-vehicle driving risk management framework. Different from existingalgorithms, the motion tendency between ego and obstacle cars and thepedestrian factor are legitimately considered in the proposed framework, whichcan improve the performance of the driving risk model. Moreover, it requiresonly O(N 2) of time complexity in the proposed method. Empirical studiesvalidate the superiority of our proposed framework against state-of-the-artmethods on real-world dataset NGSIM and real AV platform.

RCP-RF: A Comprehensive Road-car-pedestrian Risk Management Framework based on Driving Risk Potential Field