From Optimizable to Interactable: Mixed Digital Twin-Empowered Testing of Vehicle-Infrastructure Cooperation Systems

TL;DR

This paper introduces a new human-interactive digital twin framework for vehicle-infrastructure systems, enhancing corner-case testing by combining human input with virtual simulations for safer and more realistic evaluations.

Contribution

It extends the digital twin taxonomy to include human interaction at level 5 and proposes a novel testing framework that integrates human behaviors and real-world interactions into VICS testing.

Findings

Effective generation of high-quality corner cases through human interaction.

Enhanced safety and realism in VICS testing with mixed digital twin technology.

Successful implementation and validation on the I-VIT testbed.

Abstract

Sufficient testing under corner cases is critical for the long-term operation of vehicle-infrastructure cooperation systems (VICS). However, existing corner-case generation methods are primarily AI-driven, and VICS testing under corner cases is typically limited to simulation. In this paper, we introduce an L5 ''Interactable'' level to the VICS digital twin (VICS-DT) taxonomy, extending beyond the conventional L4 ''Optimizable'' level. We further propose an L5-level VICS testing framework, IMPACT (Interactive Mixed-digital-twin Paradigm for Advanced Cooperative vehicle-infrastructure Testing). By enabling direct human interactions with VICS entities, IMPACT incorporates highly uncertain and unpredictable human behaviors into the testing loop, naturally generating high-quality corner cases that complement AI-based methods. Furthermore, the mixedDT-enabled ''Physical-Virtual Action Interaction'' facilitates safe VICS testing under corner cases, incorporating real-world environments and entities rather than purely in simulation. Finally, we implement IMPACT on the I-VIT (Interactive Vehicle-Infrastructure Testbed), and experiments demonstrate its effectiveness. The experimental videos are available at our project website: https://dongjh20.github.io/IMPACT.