Probabilistic Threat Assessment with Environment Description and Rule-based Multi-traffic Prediction for Integrated Risk Management System

The objective of this paper is to propose an original probabilistic threat assessment method to completely predict and avoid all possible kinds of collision in multi-vehicle traffics. The main concerns in risk assessment can be summarized as three requirements: 1) a description of a traffic situation containing the geometric description of the road, dynamic and static obstacle tracking, 2) a prediction of multiple and 3) an assessment of collision risk which corresponds with driver sensitivity and can be applied to many complex situations without loss of generality. To fulfill these three requirements, the proposed algorithm for estimating the probability of collision occurrence of the ego vehicle follows the basic idea of the particle filtering and the collision probability can be numerically implemented and calculated. The overall performance of the proposed threat assessment algorithm is verified via vehicle tests in real road. It has been shown that the threat assessment performance for the given driving situations can be significantly enhanced by the proposed algorithm. And this enhancement of risk assessment performance led to capabilities improvement of driver assistance functions of ADASs.