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

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.

A Comprehensive Survey on Cooperative Intersection Management for Heterogeneous Connected Vehicles

“…Signal phase and timing (SPaT) control of the traffic lights is recognized as the simplest optimization-based method and can produce reasonable throughput [34], [36]- [38], [56].…”

“…Concerning delay, the proposed method had better performance than normal signalized intersections. Ashtiani et al [38] followed the similar optimization-based approach as [36], [37] for a grid of intersections that resulted in positive influence on fuel consumption and mobility of the traffic. Furthermore, Chang and Park [41] availed an optimization-based system to control the traffic signals at the intersection.…”

Energy saving potentials of connected and automated vehicles

“…In addition to minimizing intersection delay and ensuring intersection safety, the desired arrival time of the vehicles is incorporated into the optimization problem in such a way that vehicles would not face extreme delay or expedition compared to their desired arrival times. In (Ashtiani et al, 2018) the concept is extended to multiple intersections. Simulations indicate benefits of such systems greatly increase if vehicles move in platoons, in certain cases doubling the arterial network capacity with the coordination of platoons and intersections (Lioris et al, 2015).…”

Distributed Cooperative Driving in Multi-Intersection Road Networks

“…More seriously, as pointed out in [26], it may generate the causality cycles in the process of planning trajectory for vehicles, where the planning results of the vehicles around different conflict areas affect each other mutually so that it leads to failures when each vehicle plans its ultimately optimal trajectory [26]. In such case, a few studies directly deal with all vehicles in the road network and formulate a large-scale planning problem, where each conflict between vehicles introduces a binary variable to mathematically describe vehicle sequence at conflict areas [27], [28]. It leads to high computational complexity and makes the centralized planning problem intractable.…”