Leading Cruise Control in Mixed Traffic Flow: System Modeling, Controllability, and String Stability

TL;DR

This paper introduces Leading Cruise Control (LCC), a novel control strategy for connected autonomous vehicles that aims to improve traffic flow by actively managing upstream and downstream traffic perturbations through system modeling and stability analysis.

Contribution

The paper develops a new LCC framework that considers upstream and downstream traffic conditions, with analysis of controllability, observability, and string stability in mixed traffic scenarios.

Findings

LCC effectively attenuates traffic perturbations.

Incorporating information from vehicles behind enhances control performance.

LCC improves overall traffic flow stability.

Abstract

Connected and autonomous vehicles (CAVs) have great potential to improve road transportation systems. Most existing strategies for CAVs' longitudinal control focus on downstream traffic conditions, but neglect the impact of CAVs' behaviors on upstream traffic flow. In this paper, we introduce a notion of Leading Cruise Control (LCC), in which the CAV maintains car-following operations adapting to the states of its preceding vehicles, and also aims to lead the motion of its following vehicles. Specifically, by controlling the CAV, LCC aims to attenuate downstream traffic perturbations and smooth upstream traffic flow actively. We first present the dynamical modeling of LCC, with a focus on three fundamental scenarios: car-following, free-driving, and Connected Cruise Control. Then, the analysis of controllability, observability, and head-to-tail string stability reveals the feasibility and potential of LCC in improving mixed traffic flow performance. Extensive numerical studies validate that the capability of CAVs in dissipating traffic perturbations is further strengthened when incorporating the information of the vehicles behind into the CAV's control.