Operating Within the Operational Design Domain: Zero-Shot Perception with Vision-Language Models

Researchers demonstrate that vision-language models (VLMs) can effectively function as zero-shot sensors for perceiving Operational Design Domains (ODDs) in autonomous systems without task-specific training. The study evaluates four VLMs on ODD classification and detection tasks, finding that chain-of-thought prompting with persona decomposition achieves optimal performance, providing a scalable approach for safety-critical autonomous driving applications.

This research addresses a critical gap in autonomous system deployment by leveraging vision-language models as adaptable sensors for Operational Design Domain perception. ODDs define the specific environmental and operational conditions within which autonomous agents can safely function—a regulatory requirement for systems like Automated Driving Systems (ADS). Traditional approaches require extensive labeled datasets and task-specific model retraining, creating bottlenecks for deployment and regulatory compliance.

The significance of this work stems from the maturation of autonomous systems research and the regulatory landscape's increasing emphasis on safety verification. As autonomous vehicle deployments expand globally, regulators demand transparent, auditable perception of operational boundaries. VLMs present a compelling alternative because they integrate visual understanding with language reasoning, enabling zero-shot adaptation to evolving ODD definitions without retraining. This flexibility addresses a practical challenge: regulatory definitions change, new environmental conditions emerge, and different jurisdictions impose different requirements.

For developers and safety engineers, the research validates VLMs as practical tools for ODD compliance monitoring. The finding that definition-anchored chain-of-thought prompting with persona decomposition outperforms other methods provides actionable guidance for implementation. The suite of reusable prompting templates reduces development friction for teams integrating this approach.

Looking ahead, this work establishes foundations for standardized ODD perception frameworks across the autonomous systems industry. Future research should explore scaling to more complex ODD scenarios, multi-modal sensor fusion, and real-time performance constraints. As regulators increasingly demand explainable perception systems, VLM-based approaches offer transparency advantages over black-box deep learning models, potentially accelerating regulatory acceptance.

• →Vision-language models can serve as zero-shot ODD sensors without task-specific training, enabling adaptable compliance monitoring for autonomous systems.
• →Definition-anchored chain-of-thought prompting with persona decomposition consistently outperforms alternative optimization strategies across evaluated VLMs.
• →The approach addresses regulatory requirements by providing transparent, auditable perception of operational boundaries for safety-critical applications.
• →Reusable prompting templates and guidance enable practical deployment across different ODD definitions and regulatory jurisdictions.
• →VLM-based ODD perception reduces development cycles compared to traditional supervised learning approaches requiring extensive labeled datasets.