# Digital Twin-Assisted Cooperative Driving at Non-Signalized Intersections

arXiv: https://arxiv.org/abs/2105.01357 · HTML: https://mlsanity.com/paper/2105.01357/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 May 2021.

## Built on by (most cited first, 14 of 14)

- [Learning for Vehicle-to-Vehicle Cooperative Perception under Lossy Communication](https://mlsanity.com/paper/2212.08273/) (2022)
- [CitySim: A Drone-Based Vehicle Trajectory Dataset for Safety Oriented Research and Digital Twins](https://mlsanity.com/paper/2208.11036/) (2022)
- [Federated Learning for Connected and Automated Vehicles: A Survey of Existing Approaches and Challenges](https://mlsanity.com/paper/2308.10407/) (2023)
- [Towards Vehicle-to-everything Autonomous Driving: A Survey on Collaborative Perception](https://mlsanity.com/paper/2308.16714/) (2023)
- [Interruption-Aware Cooperative Perception for V2X Communication-Aided Autonomous Driving](https://mlsanity.com/paper/2304.11821/) (2023)
- [Driver Digital Twin for Online Prediction of Personalized Lane Change Behavior](https://mlsanity.com/paper/2211.01294/) (2022)
- [Car-Following Models: A Multidisciplinary Review](https://mlsanity.com/paper/2304.07143/) (2023)
- [Vision-Cloud Data Fusion for ADAS: A Lane Change Prediction Case Study](https://mlsanity.com/paper/2112.04042/) (2021)
- [Autonomous Driving at Unsignalized Intersections: A Review of Decision-Making Challenges and Reinforcement Learning-Based Solutions](https://mlsanity.com/paper/2409.13144/) (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)
- [Augmented Intelligence in Smart Intersections: Local Digital Twins-Assisted Hybrid Autonomous Driving](https://mlsanity.com/paper/2410.12163/) (2024)
- [Geo-ORBIT: A Federated Digital Twin Framework for Scene-Adaptive Lane Geometry Detection](https://mlsanity.com/paper/2507.08743/) (2025)
- [Learning Based Task Offloading in Digital Twin Empowered Internet of Vehicles](https://mlsanity.com/paper/2201.09076/) (2021)

## Abstract (arXiv)

Digital Twin, as an emerging technology related to Cyber-Physical Systems (CPS) and Internet of Things (IoT), has attracted increasing attentions during the past decade. Conceptually, a Digital Twin is a digital replica of a physical entity in the real world, and this technology is leveraged in this study to design a cooperative driving system at non-signalized intersections, allowing connected vehicles to cooperate with each other to cross intersections without any full stops. Within the proposed Digital Twin framework, we developed an enhanced first-in-first-out (FIFO) slot reservation algorithm to schedule the sequence of crossing vehicles, a consensus motion control algorithm to calculate vehicles' referenced longitudinal motion, and a model-based motion estimation algorithm to tackle communication delay and packet loss. Additionally, an augmented reality (AR) human-machine-interface (HMI) is designed to provide the guidance to drivers to cooperate with other connected vehicles. Agent-based modeling and simulation of the proposed system is conducted in Unity game engine based on a real-world map in San Francisco, and the human-in-the-loop (HITL) simulation results prove the benefits of the proposed algorithms with 20% reduction in travel time and 23.7% reduction in energy consumption, respectively, when compared with traditional signalized intersections.