# Towards Safe and Robust Autonomous Vehicle Platooning: A Self-Organizing Cooperative Control Framework

arXiv: https://arxiv.org/abs/2408.09468 · HTML: https://mlsanity.com/paper/2408.09468/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Cooperative and V2X driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 12 of its 44 references that are in the map.
- First posted on arXiv 18 Aug 2024.

## Placed in

- End-to-end autonomous driving / [Cooperative and V2X driving](https://mlsanity.com/driving/cooperative-and-v2x-driving/)

## Builds on (most cited first, 12 of 12)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Playing Atari with Deep Reinforcement Learning](https://mlsanity.com/paper/1312.5602/) (2013)
- [Trust Region Policy Optimization](https://mlsanity.com/paper/1502.05477/) (2015)
- [Asynchronous Methods for Deep Reinforcement Learning](https://mlsanity.com/paper/1602.01783/) (2016)
- [A Survey of Deep RL and IL for Autonomous Driving Policy Learning](https://mlsanity.com/paper/2101.01993/) (2021)
- [Social Coordination and Altruism in Autonomous Driving](https://mlsanity.com/paper/2107.00200/) (2021)
- [Towards Socially Responsive Autonomous Vehicles: A Reinforcement Learning Framework with Driving Priors and Coordination Awareness](https://mlsanity.com/paper/2309.09726/) (2023)
- [Generalization, Mayhems and Limits in Recurrent Proximal Policy Optimization](https://mlsanity.com/paper/2205.11104/) (2022)
- [Enhancing System-Level Safety in Mixed-Autonomy Platoon via Safe Reinforcement Learning](https://mlsanity.com/paper/2401.11148/) (2024)
- [Delay-Aware Multi-Agent Reinforcement Learning for Cooperative Adaptive Cruise Control with Model-based Stability Enhancement](https://mlsanity.com/paper/2404.15696/) (2024)
- [Learning the policy for mixed electric platoon control of automated and human-driven vehicles at signalized intersection: a random search approach](https://mlsanity.com/paper/2206.12052/) (2022)
- [Vehicles Swarm Intelligence: Cooperation in both Longitudinal and Lateral Dimensions](https://mlsanity.com/paper/2405.08039/) (2024)

## Built on by (most cited first, 2 of 2)

- [TeLL-Drive: Enhancing Autonomous Driving with Teacher LLM-Guided Deep Reinforcement Learning](https://mlsanity.com/paper/2502.01387/) (2025)
- [Enforcing Cooperative Safety for Reinforcement Learning-based Mixed-Autonomy Platoon Control](https://mlsanity.com/paper/2411.10031/) (2024)

## Abstract (arXiv)

In hybrid traffic environments where human-driven vehicles (HDVs) and autonomous vehicles (AVs) coexist, achieving safe and robust decision-making for AV platooning remains a complex challenge. Existing platooning systems often struggle with dynamic formation management and adaptability, especially under complex and dynamic mixed-traffic conditions. To enhance autonomous vehicle platooning within these hybrid environments, this paper presents TriCoD, a twin-world safety-enhanced Data-Model-Knowledge Triple-Driven Cooperative Decision-making Framework. This framework integrates deep reinforcement learning (DRL) with model-driven approaches, enabling dynamic formation dissolution and reconfiguration through a safety-prioritized twin-world deduction mechanism. The DRL component augments traditional model-driven methods, enhancing both safety and operational efficiency, especially under emergency conditions. Additionally, an adaptive switching mechanism allows the system to seamlessly switch between data-driven and model-driven strategies based on real-time traffic demands, thus optimizing decision-making ability and adaptability. Simulation experiments and hardware-in-the-loop tests demonstrate that the proposed framework significantly improves safety, robustness, and flexibility.