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.
Research has proposed a variety of cooperative systems that allow drivers to participate in the driving task. While previous work shows that such interfaces can improve user experience, trust, and acceptance, reasons for their effectiveness remain unclear. This paper investigates the effect of different components of a...
Dinara Talypova, Ambika Shahu, Philipp Wintersberger· Proceedings of the 18th Inte...· 0 citations
Driver assistance has become a safety‐critical, personalizable support system in which perception and control are coupled to driver monitoring, human–machine interfaces (HMI), while being shaped by regulatory requirements. As the sensing, perception, and control foundations of advanced driver‐assistance systems (ADAS)...
Jana Skirnewskaja, Mardavij Roozbehani, M. Dahleh· Advanced Intelligent Systems· 0 citations
A structured framework for cross-domain integration and system-level validation in SDV environments that unifies four foundational engineering capabilities: explicit modeling of inter-domain dependencies, standardized signal interface definitions aligned with AUTOSAR Adaptive specifications, cross-domain temporal synch...
Sumaiyya Fatima· International Journal of Eng...· 0 citations
The safety and reliability of Automated Driving Systems (ADS) must be validated before large-scale deployment, and scenario-based testing is a promising way to improve validation efficiency and reduce cost. However, unidentified cross-regional differences in driving scenarios force manufacturers to repeat extensive...
Ji Zhou, Yongqi Zhao, Arno Eichberger· Journal of Intelligent and C...· 0 citations
This survey synthesizes 257 papers spanning agent evaluation, software assurance, cyber-physical systems, runtime monitoring, and regulatory guidance in order to characterize the validation problem for agentic systems, and concludes with a lifecycle-oriented research agenda centered on bounded-autonomy specifications,...
Fabio Orazio Mirto, L. D'Agati, Giuseppe Tricomi et al.· arXiv.org· 0 citations
As autonomous driving systems advance beyond Level 3, traditional unidimensional evaluation methodologies are proving insufficient. A critical need exists for a reliable and trustworthy evaluation framework that aligns with human subjective judgment. Current approaches, however, often lack consensus on the relative imp...
Yan Huang, Sihan Wang, Jian Sun et al.· IEEE transactions on intelli...· 0 citations
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