From Operational Design Domain to Action: A Systematic Behavioral Taxonomy for Autonomous Driving
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 30 references that are in the map. First posted on arXiv 9 Aug 2026.
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8 of 30 references.
References · 8
- 2023Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecastingcited by 707
- 2021Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion Datasetcited by 537
- 2017On a Formal Model of Safe and Scalable Self-driving Carscited by 131
- 2018Scenarios for Development, Test and Validation of Automated Vehiclescited by 48
- 2017Ontology based Scene Creation for the Development of Automated Vehiclescited by 40
- 20206-Layer Model for a Structured Description and Categorization of Urban Traffic and Environmentcited by 26
- 2019From Functional to Logical Scenarios: Detailing a Keyword-Based Scenario Description for Execution in a Simulation Environmentcited by 5
- 2026RECTOR: Priority-Aware Rule-Based Reranking for Compliance-Aware Autonomous Driving Trajectory Selectioncited by 1
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