# Improving safety in mixed traffic: A learning-based model predictive control for autonomous and human-driven vehicle platooning

arXiv: https://arxiv.org/abs/2211.04665 · HTML: https://mlsanity.com/paper/2211.04665/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2022.

## Built on by (most cited first, 3 of 3)

- [Game-Theoretic Modeling of Vehicle Unprotected Left Turns Considering Drivers' Bounded Rationality](https://mlsanity.com/paper/2507.03002/) (2025)
- [A Tutorial on Gaussian Process Learning-based Model Predictive Control](https://mlsanity.com/paper/2404.03689/) (2024)
- [Enhancing Safety in Mixed Traffic: Learning-Based Modeling and Efficient Control of Autonomous and Human-Driven Vehicles](https://mlsanity.com/paper/2404.06732/) (2024)

## Abstract (arXiv)

As autonomous vehicles (AVs) become more common on public roads, their interaction with human-driven vehicles (HVs) in mixed traffic is inevitable. This requires new control strategies for AVs to handle the unpredictable nature of HVs. This study focused on safe control in mixed-vehicle platoons consisting of both AVs and HVs, particularly during longitudinal car-following scenarios. We introduce a novel model that combines a conventional first-principles model with a Gaussian process (GP) machine learning-based model to better predict HV behavior. Our results showed a significant improvement in predicting HV speed, with a 35.64% reduction in the root mean square error compared with the use of the first-principles model alone. We developed a new control strategy called GP-MPC, which uses the proposed HV model for safer distance management between vehicles in the mixed platoon. The GP-MPC strategy effectively utilizes the capacity of the GP model to assess uncertainties, thereby significantly enhancing safety in challenging traffic scenarios, such as emergency braking scenarios. In simulations, the GP-MPC strategy outperformed the baseline MPC method, offering better safety and more efficient vehicle movement in mixed traffic.