# A Safety-Prioritized Receding Horizon Control Framework for Platoon Formation in a Mixed Traffic Environment

arXiv: https://arxiv.org/abs/2205.10673 · HTML: https://mlsanity.com/paper/2205.10673/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2022.

## Built on by (most cited first, 1 of 1)

- [Closed-Loop Refinement and Execution for Learned Driving Planners](https://mlsanity.com/paper/2610.00992/) (2026)

## Abstract (arXiv)

Platoon formation with connected and automated vehicles (CAVs) in a mixed traffic environment poses significant challenges due to the presence of human-driven vehicles (HDVs) with unknown dynamics and control actions. In this paper, we develop a safety-prioritized receding horizon control framework for creating platoons of HDVs preceded by a CAV. Our framework ensures indirect control of the following HDVs by directly controlling the leading CAV given the safety constraints. The framework utilizes a data-driven prediction model that is based on the recursive least squares algorithm and the constant time headway relative velocity car-following model to predict future trajectories of human-driven vehicles. To demonstrate the efficacy of the proposed framework, we conduct numerical simulations and provide the associated scalability, robustness, and performance analyses.