A time-independent trajectory optimization approach for connected and autonomous vehicles under reservation-based intersection control

Abstract

Rule-based and optimization-based autonomous intersection management (AIM) policies have been evaluated against traditional signal controls in terms of intersection safety, efficiency and emission. As one of AIM policies, reservation-based control has further taken advantage of the benefits of AIM, especially via optimization approaches. This paper presents a time-independent trajectory optimization approach for connected and autonomous vehicles under reservation-based intersection control. The existing approaches assign an arrival time and speed to vehicles ahead of entering the intersection. However, the vehicles may not follow the planned trajectory once the traffic condition varies sharply and thus the trajectory solution becomes infeasible with respect to the assigned arrival time and speed. The proposed approach aims to solve the fail-follow problem by separating the optimization between arrival time, speed, and trajectory planning by optimizing the trajectory without arrival time and speed predetermined. The approach finds the optimal solution in terms of the intersection efficiency meanwhile keeps the feasibility of trajectory planning by formulating the variation of acceleration rate and breaking a whole trajectory into an enlarged set of segments. Two different control strategies, BATCH and ZONE, are also proposed to test the performance of the optimization approach in comparison with another Dynamic Batch strategy. The results validate that the proposed approach can adapt to extremely high traffic demand scenario. Sensitivity analyses also evaluate the performance of the proposed approach under different problem settings in terms of intersection efficiency.