Trustworthy and Explainable AI for Vehicle Perception and Driver Monitoring

Dear Colleagues,

The rapid evolution of Artificial Intelligence and its application to Autonomous Driving Systems (ADSs) is fundamentally reshaping research and industry trends. As we move towards higher levels of automation, the “black-box” nature of deep learning models conflicts with Trustworthy AI requirements, particularly within the European context.

Recent breakthroughs in Generative AI, and specifically Vision–Language Models (VLMs), are opening new pathways for innovation in autonomous driving. Even with the emergence of novel End-to-End (E2E) paradigms, such as Vision–Language–Action (VLA), Perception Systems (PSs) and Driver Monitoring Systems (DMSs) remain the primary challenges for any ADS. Complex traffic scenes, edge cases, human behaviour, and ever-changing real-world situations require not only the practical application of methodologies to deploy GenAI in safety-critical environments, but also the guarantee of and adherence to Trustworthy AI principles. A comprehensive analysis of limitations, capabilities, privacy, ethical aspects, transparency, and co-existence with safety systems is now essential.

We seek original research and review articles focusing on, but not limited to, the following:

• Explainable AI (XAI) for CCAM architectures, real-time perception, and DMS.
• Application frameworks for Trustworthy AI in compliance with European regulations (e.g., EU AI Act).
• Generative AI and VLM deployment and co-existence with safety-critical systems.
• Analysis of limitations and capabilities of GenAI/VLM in complex scenes, diverse human behaviour, and edge cases.
• Privacy-preserving AI: on-device processing, federated learning, and data anonymization techniques.
• Human–Machine Interaction (HMI) powered by transparent AI reasoning.

Dr. Marcos Nieto
Dr. Carlos R. del Blanco
Dr. Nerea Aranjuelo
Guest Editors

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