Digital Twin-Empowered Cooperative Autonomous Car-sharing Services: Proof-of-Concept
Kazuma Nonomura, Kui Wang, Zongdian Li, Tao Yu, Kei Sakaguchi
TL;DR
The paper tackles inefficiencies in autonomous car-sharing caused by stale data in dynamic urban traffic. It introduces a smart mobility digital twin (SMDT) that fuses RSU/CAV data into a cloud-based twin and uses AoI-aware travel times $T^{i,j}(t)$ via an AoI-weight matrix $T_{AoI}(t)$ to guide a two-phase routing strategy, balancing exploration and exploitation. Real-world validation on a university campus with two CAVs and three RSUs demonstrates practical gains: cooperative path planning yields a 22.3% reduction in delivery time compared to ego shortest-path planning, while digital-twin simulations show an 11.8% improvement in completed delivery tasks and a 23% reduction in peak AoI. These results underscore the feasibility and scalability of digital-twin-enabled cooperative driving for future smart mobility deployments, enabling more efficient and data-faithful autonomous car-sharing services.
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.