Real-time Trajectory Planning for Managing Demand and Safety during the Crossing of an Intersection by Connected Autonomous Vehicles

This work investigates the problem of autonomous intersection crossings facilitated by CAVs by presenting an optimization framework that also considers the vehicle's system state uncertainties and intersection demand management. Assuming linear-Gaussian systems, the presented approach, i.e., AVOID-OM, tackles the problem in two phases. First it decides the best arrival moment in time for a vehicle to enter the area surrounding the intersection, i.e., the danger zone. Then, it selects a set of controls for the vehicle that allows the safe traversal of the intersection, even in presence of vehicle location uncertainty. The overall objective is the maximization of the capacity of the intersection and the presented approach improves previous state-of-the-art solutions both in terms of average number of admitted vehicles and of the vehicles' average speed, while also ensuring smoothness in the vehicles' acceleration profiles.