# How Roadside Units Enhance Intersection Safety? Cooperative Autonomous Driving System Design and A Proof of Concept

arXiv: https://arxiv.org/abs/2608.09144 · HTML: https://mlsanity.com/paper/2608.09144/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Cooperative and V2X driving, one of 9 directions in End-to-end autonomous driving.
- Builds on 18 of its 52 references that are in the map.
- First posted on arXiv 10 Aug 2026.

## Placed in

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

## Builds on (most cited first, 18 of 18)

- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [High-Dimensional Continuous Control Using Generalized Advantage Estimation](https://mlsanity.com/paper/1506.02438/) (2015)
- [Conservative Q-Learning for Offline Reinforcement Learning](https://mlsanity.com/paper/2006.04779/) (2020)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [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)
- [Cooperative Decision-Making for CAVs at Unsignalized Intersections: A MARL Approach with Attention and Hierarchical Game Priors](https://mlsanity.com/paper/2409.05712/) (2024)
- [A Digital Twin Framework for Physical-Virtual Integration in V2X-Enabled Connected Vehicle Corridors](https://mlsanity.com/paper/2410.00356/) (2024)
- [Cooperative Driving of Connected Autonomous Vehicles in Heterogeneous Mixed Traffic: A Game Theoretic Approach](https://mlsanity.com/paper/2305.03563/) (2023)
- [Internet of Federated Digital Twins (IoFDT): Connecting Twins Beyond Borders for Society 5.0](https://mlsanity.com/paper/2312.06432/) (2023)
- [Architecting Digital Twins for Intelligent Transportation Systems](https://mlsanity.com/paper/2502.17646/) (2025)
- [AROW: V2X-based Automated Right-of-Way Algorithm for Cooperative Intersection Management](https://mlsanity.com/paper/2304.04958/) (2023)
- [Enhanced Cooperative Perception for Autonomous Vehicles Using Imperfect Communication](https://mlsanity.com/paper/2404.08013/) (2024)
- [Augmented Intelligence in Smart Intersections: Local Digital Twins-Assisted Hybrid Autonomous Driving](https://mlsanity.com/paper/2410.12163/) (2024)
- [The Impact of Building-Induced Visibility Restrictions on Intersection Accidents](https://mlsanity.com/paper/2503.05706/) (2025)
- [Digital Twin-based Cooperative Autonomous Driving in Smart Intersections: A Multi-Agent Reinforcement Learning Approach](https://mlsanity.com/paper/2509.15099/) (2025)

## Abstract (arXiv)

Intersections remain one of the most hazardous locations in urban road networks, where heterogeneous traffic participants and limited visibility frequently lead to severe traffic conflicts. In this paper, a vehicle-to-infrastructure-to-vehicle (V2I2V) cooperative system is proposed for improving road safety and traffic efficiency by using digital twins (DTs) deployed on roadside units (RSUs) to eliminate blind spots and centrally coordinate connected and automated vehicles (CAVs) in smart intersections. The proposed system integrates cloud-based global DTs for macroscopic guidance and RSU-based local DTs for real-time operations. Within this architecture, a hierarchical reinforcement learning (HRL) framework combines offline pre-training with online fine-tuning to achieve robust cooperative control. Experimental results show that the proposed system achieves substantial improvements in safety and efficiency in simulation experiments and real-world proof-of-concept (PoC) trials. In simulations, our system ensures high safety, efficiency, and smoothness under realistic communications and traffic constraints. In PoC trials, the RSU-centric control loop achieves a decision-making latency of approximately 42 ms and maintains a safe stopping distance of 8.5 m for pedestrians, while also shortening stop duration and overall traversal time. These results indicate that the proposed system provides robust and scalable performance at smart intersections.