Mixed Platoon Control under Noise and Attacks: Robust Data-Driven Predictive Control and Human-in-the-Loop Validation

TL;DR

This paper introduces a robust data-driven predictive control framework for mixed vehicle platoons that effectively handles noise and attacks, improving safety, efficiency, and comfort through reachability analysis and human-in-the-loop validation.

Contribution

It proposes a novel RDeeP-LCC framework using data-driven reachability analysis to enhance robustness against noise and attacks in mixed platoon control.

Findings

Significant robustness improvement against noise and attacks.

Enhanced tracking accuracy and control efficiency.

Better driving safety and comfort.

Abstract

Controlling mixed platoons, which consist of both connected and automated vehicles (CAVs) and human-driven vehicles (HDVs), poses significant challenges due to the uncertain and unknown human driving behaviors. Data-driven control methods offer promising solutions by leveraging available trajectory data, but their performance can be compromised by noise and attacks. To address this issue, this paper proposes a Robust Data-EnablEd Predictive Leading Cruise Control (RDeeP-LCC) framework based on data-driven reachability analysis. The framework over-approximates system dynamics under noise and attack using a matrix zonotope set derived from data, and develops a stabilizing feedback control law. By decoupling the mixed platoon system into nominal and error components, we employ data-driven reachability sets to recursively compute error reachable sets that account for noise and attacks, and obtain tightened safety constraints of the nominal system. This leads to a robust data-driven predictive control framework, solved in a tube-based control manner. Human-in-the-loop experiments demonstrate that the RDeeP-LCC method significantly improves robustness against noise and attacks, while enhancing tracking accuracy, control efficiency, energy economy, driving comfort, and driving safety.