Heye Huang, Korea Advanced Institute of Science and Technology (KAIST)

Inhi Kim, Korea Advanced Institute of Science and Technology (KAIST)

Boqi Li, Beihang University

Haotian Shi, Tongji University

Lei Yang, Nanyang Technological University

Kailai Sun, Massachusetts Institute of Technology

Autonomous systems operating in complex real-world environments face significant challenges in ensuring safety, reliability, and generalization. This special issue seeks research that advances trustworthy autonomy through integration of data-driven learning with structured knowledge, world models, formal verification, and uncertainty reasoning. Contributions should address both average-case and long-tail safety-critical scenarios, with emphasis on how autonomous systems can identify risks, adapt continually, and coordinate with other agents.

The scope includes autonomous vehicles, robotics, embodied AI, and other safety-critical platforms. Submissions may combine learning, optimization, control, simulation, and human-centered methods provided they clearly contribute to trustworthy autonomous behavior. The issue welcomes original research, reviews, benchmarks, and perspective articles demonstrating methodological novelty, rigorous evaluation, or insights transferable to real-world autonomous system deployment.

Appel publié par Expert Systems. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• Trustworthy autonomy and safety principles
• Uncertainty modeling and robustness
• Runtime assurance for learning-enabled systems
• Navigation and manipulation robotics
• Mobile robotics and spatial intelligence
• Perception-action learning
• Active exploration and embodied decision-making
• LLM/VLM-enabled autonomy
• Agentic AI and multimodal perception
• Tool-using agents and knowledge-enhanced models
• Foundation model adaptation for physical systems
• Risk representation and uncertainty-aware perception
• Behavior prediction and occupancy modeling
• Scene modeling and world models
• Safety-critical interaction anticipation
• Episodic and semantic memory
• Experience retrieval and in-context adaptation
• Continual learning and self-evolving autonomy
• Recursive self-improvement under safety constraints
• Safe adaptation in changing environments
• Risk-aware planning and constrained optimization
• Safe reinforcement learning
• Decision making under uncertainty
• Model-predictive control approaches
• Closed-loop safety mechanisms
• Rare-event discovery and scenario generation
• Generative simulation and counterfactual testing
• Adversarial and stress testing
• Digital twins and safety assessment
• Connected and automated vehicles
• V2X-enabled cooperation
• Multi-agent coordination
• Traffic-level autonomy and infrastructure intelligence
• System-wide mobility optimization

Timeline

• May 1, 2027: Submission deadline