# Digital Twin-based Cooperative Autonomous Driving in Smart Intersections: A Multi-Agent Reinforcement Learning Approach

arXiv: https://arxiv.org/abs/2509.15099 · HTML: https://mlsanity.com/paper/2509.15099/

## Key facts

- Cited by 1 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 11 of its 22 references that are in the map.
- First posted on arXiv 18 Sep 2025.

## Placed in

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

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

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [Conservative Q-Learning for Offline Reinforcement Learning](https://mlsanity.com/paper/2006.04779/) (2020)
- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [Actor-Attention-Critic for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1810.02912/) (2018)
- [The inD Dataset: A Drone Dataset of Naturalistic Road User Trajectories at German Intersections](https://mlsanity.com/paper/1911.07602/) (2019)
- [Smart Mobility Digital Twin Based Automated Vehicle Navigation System: A Proof of Concept](https://mlsanity.com/paper/2402.12682/) (2024)
- [Digital Twins for Autonomous Driving: A Comprehensive Implementation and Demonstration](https://mlsanity.com/paper/2401.08653/) (2023)
- [The Seven Worlds and Experiences of the Wireless Metaverse: Challenges and Opportunities](https://mlsanity.com/paper/2304.10282/) (2023)
- [Proof of Travel for Trust-Based Data Validation in V2I Communication](https://mlsanity.com/paper/2104.05070/) (2021)
- [Overcoming Blind Spots: Occlusion Considerations for Improved Autonomous Driving Safety](https://mlsanity.com/paper/2402.01507/) (2024)
- [Roadside Units Assisted Localized Automated Vehicle Maneuvering: An Offline Reinforcement Learning Approach](https://mlsanity.com/paper/2405.03935/) (2024)

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

- [How Roadside Units Enhance Intersection Safety? Cooperative Autonomous Driving System Design and A Proof of Concept](https://mlsanity.com/paper/2608.09144/) (2026)

## Abstract (arXiv)

Unsignalized intersections pose safety and efficiency challenges due to complex traffic flows and blind spots. In this paper, a digital twin (DT)-based cooperative driving system with roadside unit (RSU)-centric architecture is proposed for enhancing safety and efficiency at unsignalized intersections. The system leverages comprehensive bird-eye-view (BEV) perception to eliminate blind spots and employs a hybrid reinforcement learning (RL) framework combining offline pre-training with online fine-tuning. Specifically, driving policies are initially trained using conservative Q-learning (CQL) with behavior cloning (BC) on real datasets, then fine-tuned using multi-agent proximal policy optimization (MAPPO) with self-attention mechanisms to handle dynamic multi-agent coordination. The RSU implements real-time commands via vehicle-to-infrastructure (V2I) communications. Experimental results show that the proposed method yields failure rates below 0.03% coordinating up to three connected autonomous vehicles (CAVs), significantly outperforming traditional methods. In addition, the system exhibits sub-linear computational scaling with inference times under 40 ms. Furthermore, it demonstrates robust generalization across diverse unsignalized intersection scenarios, indicating its practicality and readiness for real-world deployment.