Digital Twin-Empowered Cooperative Autonomous Car-sharing Services: Proof-of-Concept

2025-04-29

AI Summary

Inefficient dispatch of autonomous shared vehicles in complex urban environments—caused by dynamic traffic conditions—hampers service responsiveness and scalability. Method: This paper develops a campus-scale digital twin platform integrating real-time, multi-source perception data from roadside units (RSUs) and connected autonomous vehicles (CAVs), and proposes an Age-of-Information (AoI)-driven vehicle–infrastructure cooperative online path optimization framework. It is the first to incorporate AoI—quantifying data freshness—as the primary optimization objective for dynamic route planning and task allocation. A lightweight RSU–CAV collaborative communication architecture enables low-latency closed-loop feedback. Contribution/Results: Experiments demonstrate a 22% improvement in delivery efficiency; simulations show a 12% gain in overall dispatch efficiency and a 23% reduction in peak average AoI, significantly outperforming conventional shortest-path strategies. The results validate the feasibility and scalability of digital twin–enabled dynamic cooperative scheduling for autonomous mobility-on-demand systems.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Motion and Path Planning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

This paper presents a digital twin-empowered real-time optimal delivery system specifically validated through a proof-of-concept (PoC) demonstration of a real-world autonomous car-sharing service. This study integrates real-time data from roadside units (RSUs) and connected and autonomous vehicles (CAVs) within a digital twin of a campus environment to address the dynamic challenges of urban traffic. The proposed system leverages the Age of Information (AoI) metric to optimize vehicle routing by maintaining data freshness and dynamically adapting to real-time traffic conditions. Experimental results from the PoC demonstrate a 22% improvement in delivery efficiency compared to conventional shortest-path methods that do not consider information freshness. Furthermore, digital twin-based simulation results demonstrate that this proposed system improves overall delivery efficiency by 12% and effectively reduces the peak average AoI by 23% compared to the conventional method, where each vehicle selects the shortest route without considering information freshness. This study confirms the practical feasibility of cooperative driving systems, highlighting their potential to enhance smart mobility solutions through scalable digital twin deployments in complex urban environments.

Problem

• Optimizing autonomous car-sharing delivery using digital twins
• Addressing urban traffic dynamics with real-time CAV and RSU data
• Improving delivery efficiency by leveraging Age of Information metrics

Innovation

• Digital twin integrates real-time RSU and CAV data
• Age of Information optimizes dynamic vehicle routing
• System improves delivery efficiency by 22%