Platoon Formation in a Mixed Traffic Environment: A Model-Agnostic Optimal Control Approach

TL;DR

This paper introduces a model-agnostic optimal control method for CAVs to form platoons in mixed traffic, effectively managing human-driven vehicles without relying on explicit HDV models.

Contribution

It develops a novel multi-objective control framework that directly controls the CAV leader to form platoons without explicit HDV dynamics knowledge.

Findings

Effective platoon formation demonstrated in simulations

Robustness to variations in human driver behavior

Enhanced safety and efficiency in mixed traffic environments

Abstract

Coordination of connected and automated vehicles (CAVs) in a mixed traffic environment poses significant challenges due to the presence of human-driven vehicles (HDVs) with stochastic dynamics and driving behavior. In earlier work, we addressed the problem of platoon formation of HDVs led by a CAV using a model-dependent controller. In this paper, we develop a comprehensive model-agnostic, multi-objective optimal controller which ensures platoon formation by directly controlling the leading CAV without having explicit knowledge of the trailing HDV dynamics. We provide a detailed exposition of the control framework that uses instantaneous motion information from multiple successive HDVs to enforce safety while achieving the optimization objectives. To demonstrate the efficacy of the proposed control framework, we evaluate its performance using numerical simulation and provide associated sensitivity and robustness analysis.