# DeeP-LCC: Data-EnablEd Predictive Leading Cruise Control in Mixed Traffic Flow

arXiv: https://arxiv.org/abs/2203.10639 · HTML: https://mlsanity.com/paper/2203.10639/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2022.

## Built on by (most cited first, 8 of 8)

- [LanguageMPC: Large Language Models as Decision Makers for Autonomous Driving](https://mlsanity.com/paper/2310.03026/) (2023)
- [Enhancing System-Level Safety in Mixed-Autonomy Platoon via Safe Reinforcement Learning](https://mlsanity.com/paper/2401.11148/) (2024)
- [Urban traffic congestion control: a DeePC change](https://mlsanity.com/paper/2311.09851/) (2023)
- [Reinforcement Learning for Freeway Lane-Change Regulation via Connected Vehicles](https://mlsanity.com/paper/2412.04341/) (2024)
- [Logarithmic Regret and Polynomial Scaling in Online Multi-step-ahead Prediction](https://mlsanity.com/paper/2511.12467/) (2025)
- [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)
- [Data-driven generalized perimeter control: Z"urich case study](https://mlsanity.com/paper/2603.16599/) (2026)

## Abstract (arXiv)

For the control of connected and autonomous vehicles (CAVs), most existing methods focus on model-based strategies. They require explicit knowledge of car-following dynamics of human-driven vehicles that are non-trivial to identify accurately. In this paper, instead of relying on a parametric car-following model, we introduce a data-driven non-parametric strategy, called DeeP-LCC (Data-EnablEd Predictive Leading Cruise Control), to achieve safe and optimal control of CAVs in mixed traffic. We first utilize Willems' fundamental lemma to obtain a data-centric representation of mixed traffic behavior. This is justified by rigorous analysis on controllability and observability properties of mixed traffic. We then employ a receding horizon strategy to solve a finite-horizon optimal control problem at each time step, in which input/output constraints are incorporated for collision-free guarantees. Numerical experiments validate the performance of DeeP-LCC compared to a standard predictive controller that requires an accurate model. Multiple nonlinear traffic simulations further confirm its great potential on improving traffic efficiency, driving safety, and fuel economy.