Optimization-based Heuristic for Vehicle Dynamic Coordination in Mixed Traffic Intersections

TL;DR

This paper introduces a fast, optimization-based heuristic for coordinating vehicles at unsignalized intersections, improving safety and efficiency in mixed traffic with autonomous and human-driven vehicles.

Contribution

A novel heuristic method that significantly reduces computation time for vehicle coordination at intersections, maintaining near-optimal solutions compared to traditional MIQP approaches.

Findings

Heuristic is up to 100 times faster than MIQP methods.

Solutions are close to optimal with improved order consistency.

Effective in mixed traffic scenarios with CAVs and HDVs.

Abstract

In this paper, we address a coordination problem for connected and autonomous vehicles (CAVs) in mixed traffic settings with human-driven vehicles (HDVs). The main objective is to have a safe and optimal crossing order for vehicles approaching unsignalized intersections. This problem results in a mixed-integer quadratic programming (MIQP) formulation which is unsuitable for real-time applications. Therefore, we propose a computationally tractable optimization-based heuristic that monitors platoons of CAVs and HDVs to evaluate whether alternative crossing orders can perform better. It first checks the future constraint violation that consistently occurs between pairs of platoons to determine a potential swap. Next, the costs of quadratic programming (QP) formulations associated with the current and alternative orders are compared in a depth-first branching fashion. In simulations, we show that the heuristic can be a hundred times faster than the original and simplified MIQPs and yields solutions that are close to optimal and have better order consistency.