Coordinating Multiple Cooperative Vehicle Trajectories on Shared Road Networks

This paper addresses the problem of planning trajectories for multiple cooperative agents along specified paths through a static road network. Vehicle trajectories are coupled through interactions at shared areas, including intersections and road segments. A motion model is presented that includes the combined trajectories of all vehicles. The trajectories are optimized for travel time by solving a Mixed Integer Linear Program (MILP) that incorporates constraints to ensure collision avoidance. To improve the computation time of solving the MILP, two model modifications are introduced related to interactions: 1) The use of binary variables is reduced by grouping them at targeted travel times. 2) The relative travel order of interacting vehicles is constrained, guided by a heuristic solver. To further reduce computation time, an iterative method solves a sequence of smaller MILPs by targeting essential vehicle interactions with an algorithm that predicts interactions. Experiments are run on a simulated road network based on a real surface mine and haul truck trips. The proposed MILP methods are shown to significantly reduce solution costs compared to a reactive approach based on common driving practices. The MILP modifications significantly reduce computation time, trading off marginal solution quality. The reduction is extended by the iterative MILP scheme without degradation of solution quality.