# Digital Twins for Autonomous Driving: A Comprehensive Implementation and Demonstration

arXiv: https://arxiv.org/abs/2401.08653 · HTML: https://mlsanity.com/paper/2401.08653/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Dec 2023.

## Built on by (most cited first, 10 of 10)

- [Knowledge Transfer from Simple to Complex: A Safe and Efficient Reinforcement Learning Framework for Autonomous Driving Decision-Making](https://mlsanity.com/paper/2410.14468/) (2024)
- [Roadside Units Assisted Localized Automated Vehicle Maneuvering: An Offline Reinforcement Learning Approach](https://mlsanity.com/paper/2405.03935/) (2024)
- [Multi-Agent Reinforcement Learning-based Cooperative Autonomous Driving in Smart Intersections](https://mlsanity.com/paper/2505.04231/) (2025)
- [AI-Driven HSI: Multimodality, Fusion, Challenges, and the Deep Learning Revolution](https://mlsanity.com/paper/2502.06894/) (2025)
- [Digital Twin-based Cooperative Autonomous Driving in Smart Intersections: A Multi-Agent Reinforcement Learning Approach](https://mlsanity.com/paper/2509.15099/) (2025)
- [AI-driven visual monitoring of industrial assembly tasks](https://mlsanity.com/paper/2506.15285/) (2025)
- [Digital-Twin Losses for Lane-Compliant Trajectory Prediction at Urban Intersections](https://mlsanity.com/paper/2603.05546/) (2026)
- [From Virtual Environments to Real-World Trials: Emerging Trends in Autonomous Driving](https://mlsanity.com/paper/2603.17714/) (2026)
- [EMMA: Extracting Multiple physical parameters from Multimodal Data](https://mlsanity.com/paper/2605.24047/) (2026)
- [A Risk-Field Enhanced Closed-Loop Digital Twin Framework for Autonomous Driving Safety Validation](https://mlsanity.com/paper/2607.09772/) (2026)

## Abstract (arXiv)

The concept of a digital twin (DT) plays a pivotal role in the ongoing digital transformation and has achieved significant strides for various wireless applications in recent years. In particular, the field of autonomous vehicles is a domain that is ripe for exploiting the concept of DT. Nevertheless, there are many challenges that include holistic consideration and integration of hardware, software, communication methods, and collaboration of edge/cloud computing. In this paper, an end-to-end (E2E) real-world smart mobility DT is designed and implemented for the purpose of autonomous driving. The proposed system utilizes roadside units (RSUs) and edge computing to capture real-world traffic information, which is then processed in the cloud to create a DT model. This DT model is then exploited to enable route planning services for the autonomous vehicle to avoid heavy traffic. Real-world experimental results show that the system reliability can reach 99.53% while achieving a latency that is 3.36% below the 3GPP recommended value of 100 ms for autonomous driving. These results clearly validate the effectiveness of the system according to practical 3GPP standards for sensor and state map sharing (SSMS) and information sharing.