A probabilistic approach for collision avoidance of uncertain moving objects within black zones

This work addresses the problem of collision avoidance for vehicles in black zones where the risk of accidents increases. Vehicles have an inherent uncertainty of location because the exact position of a moving object is known, with certainty, only at the time of an update on position information. This paper proposes a collision prediction model and a monitoring algorithm for collision avoidance within black zones by considering the location uncertainty of moving vehicles. It formalizes the impact of trajectories of moving vehicles on probabilistic collision prediction. The proposed approach provides approximate answers to the user at the user's required level of accuracy while achieving near-optimal communication and computational costs. Finally, extensive experiments were conducted to show the efficiency and efficacy of the proposed approach.