Digital Twin Technologies and Their Applications in Autonomous Vehicles
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".
Deadline for manuscript submissions: closed (30 June 2026) | Viewed by 954
Editor
Special Issue Information
Dear Colleagues,
This Special Issue delves into the systematic innovations brought about by digital twin technology in the realm of autonomous driving, enabling the entire chain from simulation development to real-world deployment. Areas of focus include, but are not limited to, the following directions:
- Simulation–reality fusion technologies: High-fidelity scene modeling based on physics engines and generative AI, sensor data synthesis, cross-domain data alignment and learning, sensor data processing, and quantitative assessment of simulation credibility.
- Safety validation and system evolution: Utilizing digital twins to construct critical scenarios (such as adverse weather, complex interactions, extreme driving conditions and AI security in AVs), virtual testing environments based on X-in-the-Loop, the evolution of autonomous driving systems driven by twin environments, and safety boundary extrapolation.
- Innovations in cutting-edge applications: Digital-twin-driven collaboration between vehicle, road, and cloud, end-to-end model training and deployment through virtual–real interactions, scenario generation and accountability tracing based on causal inference, and mechanisms for integrating digital twins with regulatory certification.
- Lifelong learning: Digital-twin-driven incremental and online learning, continual learning methods based on digital twins, task transfer and cross-domain generalization enabled by simulation-reality fusion, and continual evolution approaches for digital twin systems.
- Other fundamental supporting technologies: Multi-physics modeling techniques, high-fidelity simulation engines, data analytics and intelligent algorithms, as well as system integration and collaborative technologies.
This Special Issue encourages interdisciplinary research, covering topics such as algorithm innovation, engineering practice, ethical validation, and common key technologies, aiming to provide a reliable transition pathway for autonomous driving systems from the laboratory to open roads.
Dr. Ying Wang
Guest Editor
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Keywords
- digital twins
- autonomous vehicle simulation
- generative AI for synthetic data
- scenario generation
- sensor modeling
- sensor data processing
- AI security
- weakly supervised domain adaptation
- closed-loop validation
- V2X testing
- lifelong learning
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