Probabilistic Collision Threat Assessment for Autonomous Driving at Road Intersections Inclusive of Vehicles in Violation of Traffic Rules

Abstract

In this paper, we propose a probabilistic collision threat assessment algorithm for autonomous driving at road intersections that assesses a given traffic situation at an intersection reliably and robustly for an autonomous vehicle to cross the intersection safely, even in the face of violation vehicles (that is, vehicles in violation of traffic rules at the intersection). To this end, the proposed algorithm employs a detailed digital map to predict future paths of observed vehicles and then utilizes the predicted future paths to identify potential threats (vehicles) and potential collision areas, regardless of whether observed vehicles are obeying traffic rules at the intersection. Next, by means of Bayesian networks and time window filtering under an independent and distributed reasoning structure, it assesses the potential threats regarding the possibility of collision reliably and robustly, even under uncertain and incomplete noise data. Then, it has been tested and evaluated through in-vehicle testing on a closed urban test road under traffic conditions inclusive of non-violation and violation vehicles. In-vehicle testing results show that the performance of the proposed algorithm is sufficiently reliable to be used in decision-making for autonomous driving at intersections in terms of reliability and robustness, even in the face of violation vehicles.