A heuristic for optimal coordination of autonomous vehicles: dynamic problems

Abstract

The coordination of autonomous vehicles at unsignalized intersections requires deciding both the trajectories of the vehicles and their crossing order, with the additional difficulty that the set of vehicles to be coordinated changes as traffic flows through the intersection. Since the resulting mixed-integer optimal control problem is hard to solve online, we propose an arrival-triggered receding-horizon scheme built around a schedule-refinement heuristic: for a fixed crossing order, a single nonlinear program (NLP) optimizes all trajectories and crossing times, and the first-order sensitivities of the cost with respect to the vehicle schedule are used to select adjacent swaps that are likely to reduce the overall cost. Replanning is triggered only when a new vehicle arrives, the new vehicle is appended at the tail of the current crossing order, and a memory of evaluated swaps that persists across replanning steps avoids redundant computations. Simulations of traffic scenarios involving streams of a few hundred heterogeneous vehicles show that the proposed scheme recovers most of the optimality gap incurred by first-in-first-out scheduling.