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

Abstract

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-DM, 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.

Periodic and Event-Triggering for Joint Capacity Maximization and Safe Intersection Crossing

“…Being managed by the IM, with the chosen controls, it is assumed that CAVs only travel on north-to-south/southto-north or west-to-east/east-to-west directions and do not change lanes while traversing the intersection. Thus, Γ and Φ represent the unidimensional linear relationship between x j τ and pair x j τ −1 -u j τ −1 [9]. The dynamics of each CAV observe the Markov property (Eq.…”

“…Finally, as in our work in [9], with Gaussian measurement errors associated with the on-board sensors and GPS measurements through Kalman Filter (KF), CAV j obtains at any time τ an estimation of its own system state which follows a multivariate Gaussian distribution with mean µ τ and covariance matrix Ξ τ . Such estimation is communicated to the IM and can be used as x 0 and Σ 0 in Eq.…”

“…Other approaches model the uncertainty of human-driven vehicles at intersections during trajectory planning [8]. While all previous works provided useful insights, when location uncertainty is accounted for, only our work in [9] considers a framework that optimizes the choices of CAVs aiming to maximize intersection capacity.…”

“…Contrary to our approach in [9], AVOID-PERIOD exploits the communication between the IM and the CAVs; instead of optimizing the controls of the CAVs only once (at the entrance to the area supervised by the IM), it utilizes updated system state estimations transmitted by CAVs to periodically recompute their controls. Indeed, when compared with the corresponding forecasted system states, the ones transmitted by the CAVs exhibit less uncertainty and propagate a smaller error to future time instances, improving intersection capacity without compromising safety.…”

“…• builds on our uncertainty-aware mathematical framework in [9], i.e., a periodic (the AVOID-PERIOD) control optimization which enables to exploit an updated and less error-prone view of the intersection; • develops an event-triggering-based optimization (i.e., AVOID-EVENT) which provides a trade-off between performance and computational/communication complexity;…”