From Operational Design Domain to Action: A Systematic Behavioral Taxonomy for Autonomous Driving

2026-08-09

AI Summary

This study addresses the lack of explicit behavioral specifications and validation criteria for autonomous driving systems within their Operational Design Domain (ODD). Building upon the PEGASUS six-layer model, the authors propose a comprehensive behavioral capability taxonomy encompassing 21 capabilities across three key scenarios—highway, urban, and interchange environments—structured along longitudinal and lateral control dimensions and characterized by four attributes: safety, compliance, comfort, and efficiency. The work innovatively establishes a cross-mapping between parameterized ODD definitions and behavioral specifications, thereby introducing, for the first time, a verifiable and testable behavioral specification layer. Notably, interchange scenarios are identified as a structurally distinct and underexplored domain. Leveraging a rule-driven trajectory optimization system and aligned with standards such as SAE J3016, the proposed framework enables standardized, actionable behavioral capability assessment, supports SOTIF-compliant evidence generation, and demonstrates practical efficacy as an operational specification layer in real-world deployments.

Abstract

Operational Design Domain (ODD) specifications describe where an automated driving system (ADS) is permitted to operate, but they do not prescribe what the ADS must demonstrably do once deployed within that domain. This gap between operating condition specification and behavioral validation represents a critical unresolved challenge in ADS safety assurance. This paper presents a structured, standards-grounded taxonomy of 21 behavioral competencies organized across three operational domains-Highway (HWY), Urban (URB), and Hub (HUB)-derived systematically from the PEGASUS six-layer model-based ODD. Each behavior is decomposed along longitudinal and lateral control axes and characterized against a four-property framework: Safety (gap maintenance, conflict avoidance, kinematic stability), Compliance (legal rules and behavioral norms), Comfort (rider dynamics and trust), and Efficiency (mission completion and product-level metrics). We further demonstrate that the crossing of ODD layer parameterizations with behavioral competency specifications yields concrete scenario families suitable for systematic behavioral testing and SOTIF coverage evidence. The taxonomy is grounded in AVSC00008202111, SAE J3237, and SAE J3016, and is validated as an operational specification layer through its deployment in a rule-enforced trajectory optimization system. The Hub domain is identified as a structurally distinct, underspecified domain warranting dedicated research attention.

Problem

• Operational Design Domain
• Autonomous Driving
• Behavioral Validation
• Safety Assurance
• Scenario Testing

Innovation

• Behavioral Taxonomy
• Operational Design Domain (ODD)
• Autonomous Driving
• SOTIF
• Trajectory Optimization