Distributed data-driven predictive control for cooperatively smoothing mixed traffic flow

TL;DR

This paper introduces a distributed data-driven predictive control method for mixed traffic flow that enhances scalability, efficiency, and privacy, demonstrating significant fuel savings in large-scale simulations with mixed vehicle types.

Contribution

It proposes a novel distributed DeeP-LCC framework using Willems' lemma and ADMM, enabling scalable, privacy-preserving control of mixed traffic with multiple CAVs.

Findings

Reduces fuel consumption by over 31.84% in large-scale simulations.

Achieves real-time wave-dampening in mixed traffic systems.

Enhances scalability and privacy in cooperative vehicle control.

Abstract

Cooperative control of connected and automated vehicles (CAVs) promises smoother traffic flow. In mixed traffic, where human-driven vehicles with unknown dynamics coexist, data-driven predictive control techniques allow for CAV safe and optimal control with measurable traffic data. However, the centralized control setting in most existing strategies limits their scalability for large-scale mixed traffic flow. To address this problem, this paper proposes a cooperative DeeP-LCC (Data-EnablEd Predictive Leading Cruise Control) formulation and its distributed implementation algorithm. In cooperative DeeP-LCC, the traffic system is naturally partitioned into multiple subsystems with one single CAV, which collects local trajectory data for subsystem behavior predictions based on the Willems' fundamental lemma. Meanwhile, the cross-subsystem interaction is formulated as a coupling constraint. Then, we employ the Alternating Direction Method of Multipliers (ADMM) to design the distributed DeeP-LCC algorithm. This algorithm achieves both computation and communication efficiency, as well as trajectory data privacy, through parallel calculation. Our simulations on different traffic scales verify the real-time wave-dampening potential of distributed DeeP-LCC, which can reduce fuel consumption by over 31.84% in a large-scale traffic system of 100 vehicles with only 5%-20% CAVs.