Multi-Intersection Traffic Management for Autonomous Vehicles via Distributed Mixed Integer Linear Programming

TL;DR

This paper presents a scalable distributed MILP approach for optimal autonomous vehicle scheduling across multiple intersections, improving traffic flow and fuel efficiency in a grid network.

Contribution

It extends previous single-intersection scheduling methods to a grid of intersections using a distributed MILP framework with real-time coordination.

Findings

Improved traffic flow in a 3x3 intersection grid.

Enhanced fuel economy for autonomous vehicles.

Scalable distributed control approach demonstrated.

Abstract

This paper extends our previous work in [1],[2], on optimal scheduling of autonomous vehicle arrivals at intersections, from one to a grid of intersections. A scalable distributed Mixed Integer Linear Program (MILP) is devised that solves the scheduling problem for a grid of intersections. A computational control node is allocated to each intersection and regularly receives position and velocity information from subscribed vehicles. Each node assigns an intersection access time to every subscribed vehicle by solving a local MILP. Neighboring intersections will coordinate with each other in real-time by sharing their solutions for vehicles' access times with each other. Our proposed approach is applied to a grid of nine intersections and its positive impact on traffic flow and vehicles' fuel economy is demonstrated in comparison to conventional intersection control scenarios.