  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.
