Traffic Control in a Mixed Autonomy Scenario at Urban Intersections: An Optimization-based Framework

TL;DR

This paper presents an optimization-based framework for managing traffic at urban intersections with mixed autonomous and human-driven vehicles, focusing on optimal sequencing and communication constraints.

Contribution

It introduces a scalable algorithm for optimal vehicle sequencing in mixed traffic, relaxing the complex optimization problem for practical implementation.

Findings

Algorithm outperforms FIFO in simulations

Scalable solution for mixed autonomy traffic control

Effective management with limited communication to AVs

Abstract

We consider an intersection zone where autonomous vehicles (AVs) and human-driven vehicles (HDVs) can be present. As a new vehicle arrives, the traffic controller needs to decide and impose an optimal sequence of the vehicles that will exit the intersection zone. The traffic controller can send information regarding the time at which an AV can cross the intersection; however, the traffic controller can not communicate with the HDVs, rather the HDVs can only be controlled using the traffic lights. We formulate the problem as an integer constrained non-linear optimization problem where the traffic-intersection controller only communicates with a subset of the AVs. Since the number of possible combinations increases exponentially with the number of vehicles in the system, we relax the original problem and proposes an algorithm that gives the optimal solution of the relaxed problem and yet only scales linearly with the number of vehicles in the system. The numerical evaluation shows that our algorithm outperforms the First-In-First-Out (FIFO) algorithm.