# Enhancing System-Level Safety in Mixed-Autonomy Platoon via Safe Reinforcement Learning

arXiv: https://arxiv.org/abs/2401.11148 · HTML: https://mlsanity.com/paper/2401.11148/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods.
- Builds on 11 of its 43 references that are in the map.
- First posted on arXiv 20 Jan 2024.

## Placed in

- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

## Builds on (most cited first, 11 of 11)

- [A Physics-Informed Deep Learning Paradigm for Car-Following Models](https://mlsanity.com/paper/2012.13376/) (2020)
- [DeeP-LCC: Data-EnablEd Predictive Leading Cruise Control in Mixed Traffic Flow](https://mlsanity.com/paper/2203.10639/) (2022)
- [Mixed platoon control of automated and human-driven vehicles at a signalized intersection: dynamical analysis and optimal control](https://mlsanity.com/paper/2010.16105/) (2020)
- [Sufficient Conditions for Feasibility of Optimal Control Problems Using Control Barrier Functions](https://mlsanity.com/paper/2011.08248/) (2020)
- [Decentralized Optimal Merging Control for Connected and Automated Vehicles](https://mlsanity.com/paper/1809.07916/) (2018)
- [Leading Cruise Control in Mixed Traffic Flow: System Modeling, Controllability, and String Stability](https://mlsanity.com/paper/2012.04313/) (2020)
- [Data-Driven Predictive Control for Connected and Autonomous Vehicles in Mixed Traffic](https://mlsanity.com/paper/2110.10097/) (2021)
- [Feasibility Guaranteed Traffic Merging Control Using Control Barrier Functions](https://mlsanity.com/paper/2203.04348/) (2022)
- [Safety-Critical Traffic Control by Connected Automated Vehicles](https://mlsanity.com/paper/2301.04833/) (2023)
- [Energy-efficient Connected Cruise Control with Lean Penetration of Connected Vehicles](https://mlsanity.com/paper/2205.03473/) (2022)
- [Adaptive Leading Cruise Control in Mixed Traffic Considering Human Behavioral Diversity](https://mlsanity.com/paper/2210.02147/) (2022)

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

- [VLM-SAFE: Vision-Language Model-Guided Safety-Aware Reinforcement Learning with World Models for Autonomous Driving](https://mlsanity.com/paper/2505.16377/) (2025)
- [Dynamic High-Order Control Barrier Functions with Diffuser for Safety-Critical Trajectory Planning at Signal-Free Intersections](https://mlsanity.com/paper/2412.00162/) (2024)
- [Agile Decision-Making and Safety-Critical Motion Planning for Emergency Autonomous Vehicles](https://mlsanity.com/paper/2409.08665/) (2024)
- [Towards Safe and Robust Autonomous Vehicle Platooning: A Self-Organizing Cooperative Control Framework](https://mlsanity.com/paper/2408.09468/) (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)
- [Hierarchical Decision Making with Structured Policies: A Principled Design via Inverse Optimization](https://mlsanity.com/paper/2606.28764/) (2026)

## Abstract (arXiv)

Connected and automated vehicles (CAVs) have recently gained prominence in traffic research due to advances in communication technology and autonomous driving. Various longitudinal control strategies for CAVs have been developed to enhance traffic efficiency, stability, and safety in mixed-autonomy scenarios. Deep reinforcement learning (DRL) is one promising strategy for mixed-autonomy platoon control, thanks to its capability of managing complex scenarios in real time after sufficient offline training. However, there are three research gaps for DRL-based mixed-autonomy platoon control: (i) the lack of theoretical collision-free guarantees, (ii) the widely adopted but impractical assumption of skilled and rational drivers who will not collide with preceding vehicles, and (iii) the strong assumption of a known human driver model. To address these research gaps, we propose a safe DRL-based controller that can provide a system-level safety guarantee for mixed-autonomy platoon control. First, we combine control barrier function (CBF)-based safety constraints and DRL via a quadratic programming (QP)-based differentiable neural network layer to provide theoretical safety guarantees. Second, we incorporate system-level safety constraints into our proposed method to account for the safety of both CAVs and the following HDVs to address the potential collisions due to irrational human driving behavior. Third, we devise a learning-based system identification approach to estimate the unknown human car-following behavior in the real system. Simulation results demonstrate that our proposed method effectively ensures CAV safety and improves HDV safety in mixed platoon environments while simultaneously enhancing traffic capacity and string stability.