Optimal Control of Connected and Automated Vehicles at Multiple Adjacent Intersections

TL;DR

This paper presents a decentralized optimal control framework for connected and automated vehicles crossing multiple intersections, aiming to minimize energy use and maximize traffic throughput through a two-layer planning approach.

Contribution

It introduces a novel bi-level control framework that optimizes both intersection arrival times and vehicle control inputs, including a bounded steady-state error extension.

Findings

Framework reduces energy consumption compared to traditional methods.

Improves traffic throughput at multiple intersections.

Effective in both symmetric and asymmetric intersection scenarios.

Abstract

In this paper, we establish a decentralized optimal control framework for connected and automated vehicles (CAVs) crossing multiple adjacent, multi-lane signal-free intersections to minimize energy consumption and improve traffic throughput. Our framework consists of two layers of planning. In the upper-level planning, each CAV computes its optimal arrival time at each intersection recursively along with the optimal lane to improve the traffic throughput. In the low-level planning, we formulate an energy-optimal control problem with interior-point constraints, the solution of which yields the optimal control input (acceleration/deceleration) of each CAV to cross the intersections at the time specified by the upper-level planning. Moreover, we extend the results of the proposed bi-level framework to include a bounded steady-state error in tracking the optimal position of the CAVs. Finally, we demonstrate the effectiveness of the proposed framework through simulation for symmetric and asymmetric intersections and comparison with traditional signalized intersections.