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

arXiv: https://arxiv.org/abs/2012.04313 · HTML: https://mlsanity.com/paper/2012.04313/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2020.

## Built on by (most cited first, 4 of 4)

- [Enhancing System-Level Safety in Mixed-Autonomy Platoon via Safe Reinforcement Learning](https://mlsanity.com/paper/2401.11148/) (2024)
- [Enforcing Cooperative Safety for Reinforcement Learning-based Mixed-Autonomy Platoon Control](https://mlsanity.com/paper/2411.10031/) (2024)
- [Mitigating Traffic Oscillations in Mixed Traffic Flow with Scalable Deep Koopman Predictive Control](https://mlsanity.com/paper/2502.00043/) (2025)
- [Interaction-Aware Parameter Privacy-Preserving Data Sharing in Coupled Systems via Particle Filter Reinforcement Learning](https://mlsanity.com/paper/2505.06122/) (2025)

## Abstract (arXiv)

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.