Advanced AI Techniques for Trustworthy and Practical Unmanned Vehicles
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 15 April 2026 | Viewed by 20
Special Issue Editors
Interests: trustworthy AI in multimodal (e.g., adversarial examples/physical adversarial attacks/adversarial defense/backdoor detection/deepfake detection)
Special Issues, Collections and Topics in MDPI journals
Interests: AI robustness and forensics, including deepfake detection, open-world learning, copyright preservation through mechanisms like backdoor attacks, etc.
Interests: efficient machine learning, including deep model compression, efficient generative models, embodied computing, low-power embedded systems, etc.
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Unmanned vehicles have gained widespread acceptance, with their capabilities advancing significantly. Some autonomous vehicles are now capable of achieving L4+ level autonomy in complex scenarios. In recent years, rapid progress in artificial intelligence technologies, such as large-scale models and world models, has further accelerated the development of autonomous driving, bringing fully driverless operations seemingly within reach. However, integrating advanced AI technologies into autonomous vehicles presents several challenges: (1) The safety, security, and reliability of AI models remain unresolved issues, exemplified by threats such as adversarial examples; (2) There is an increasingly demanding requirement for real-time inference in edge-computing scenarios, particularly under high-speed driving conditions. These challenges necessitate further exploration of more trustworthy and efficient intelligent technologies for autonomous vehicles.
For this special issue, we aim to bring together experts and scholars from fields such as autonomous driving, practical machine learning, and trustworthy machine learning to explore the latest advances and future directions in intelligent technologies for autonomous vehicles. We invite submissions on any aspect of autonomous driving, efficient machine learning, and trustworthy machine learning. We welcome research contributions related to the following (but not limited to) topics:
- Adversarial learning (attacks, defenses in computer vision or speech);
- Continual Learning;
- Large-scale Models for Autonomous Driving;
- LLM Jailbreaking;
- Lightweight Large-scale Models;
- World Model-Driven Autonomous Driving Decision-Making;
- Data Theft and Privacy Protection;
- Explainable and practical AI;
- Autonomous Driving Safety Assessment.
Dr. Jiakai Wang
Dr. Renshuai Tao
Dr. Haotong Qin
Guest Editors
Manuscript Submission Information
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Keywords
- unmanned vehicle
- trustworthy AI
- large models
- practical AI
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