Joint optimization for autonomous intersection management and trajectory smoothing design with connected automated vehicles

Trajectory smoothing design (TSD) may significantly reduce fuel consumption and improve driving comfort at intersections. In this paper, a mixed integer linear programming (MILP) model with discrete time is formulated to jointly optimize autonomous intersection management and TSD, aiming to improve traffic efficiency, fuel economy and driving comfort simultaneously. Driving safety of car-following and collision avoidance at conflict points, diverge points and converge points, as well as constraints of acceleration and jerk are considered. To reasonably describe vehicle movement within intersection areas, the vehicle trajectory within the intersection is treated as a channel considering the vehicle width. A rolling horizon framework is used to solve the model. We have compared the traffic efficiency, fuel economy, monetary cost and driving comfort of the joint optimization model with that of the state-of-the-art two-stage strategy. Finally, sensitivity analysis with respect to left-turn ratio, weighted coefficient of TSD and control zone length is conducted.

Automated summary

In this paper, a mixed integer linear programming (MILP) model is proposed to optimize autonomous intersection management and trajectory smoothing design (TSD) simultaneously. The model takes into account driving safety, constraints, and the vehicle trajectory within the intersection. A rolling horizon framework is used to solve the model. The joint optimization model is compared with a two-stage strategy in terms of traffic efficiency, fuel economy, monetary cost, and driving comfort.