A new Taxonomy for Automated Driving: Structuring Applications based on their Operational Design Domain, Level of Automation and Automation Readiness
Not on the map. Why?
Abstract (arXiv)
The aim of this paper is to investigate the relationship between operational design domains (ODD), automated driving SAE Levels, and Technology Readiness Level (TRL). The first highly automated vehicles, like robotaxis, are in commercial use, and the first vehicles with highway pilot systems have been delivered to private customers. It has emerged as a crucial issue that these automated driving systems differ significantly in their ODD and in their technical maturity. Consequently, any approach to compare these systems is difficult and requires a deep dive into defined ODDs, specifications, and technologies used. Therefore, this paper challenges current state-of-the-art taxonomies and develops a new and integrated taxonomy that can structure automated vehicle systems more efficiently. We use the well-known SAE Levels 0-5 as the "level of responsibility", and link and describe the ODD at an intermediate level of abstraction. Finally, a new maturity model is explicitly proposed to improve the comparability of automated vehicles and driving functions. This method is then used to analyze today's existing automated vehicle applications, which are structured into the new taxonomy and rated by the new maturity levels. Our results indicate that this new taxonomy and maturity level model will help to differentiate automated vehicle systems in discussions more clearly and to discover white fields more systematically and upfront, e.g. for research but also for regulatory purposes.
Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Apr 2024.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2025: 5; 2026: 2.
7 papers.
- 2025Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysiscited by 37
- 2025From Words to Collisions: LLM-Guided Evaluation and Adversarial Generation of Safety-Critical Driving Scenarioscited by 8
- 2025NuRisk: A Visual Question Answering Dataset for Agent-Level Risk Assessment in Autonomous Drivingcited by 6
- 2025A Framework for Responsible AI Systems: Building Societal Trust through Domain Definition, Trustworthy AI Design, Auditability, Accountability, and Governancecited by 5
- 2025Pedestrian-Aware Motion Planning for Autonomous Driving in Complex Urban Scenarioscited by 2
- 2026Chat2Scenic: An Iterative RAG-Based Framework for Scenario Generation in Autonomous Drivingcited by 1
- 2026PlannerForge: LLM Agents for Scenario-Based Testing of Motion Planners in Autonomous Drivingcited by 0
- 2026PlannerForge: LLM Agents for Scenario-Based Testing of Motion Planners in Autonomous Driving8 Sep 2026
- 2026Chat2Scenic: An Iterative RAG-Based Framework for Scenario Generation in Autonomous Driving15 Jul 2026
- 2025NuRisk: A Visual Question Answering Dataset for Agent-Level Risk Assessment in Autonomous Driving30 Sep 2025
- 2025Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis13 Jun 2025
- 2025Pedestrian-Aware Motion Planning for Autonomous Driving in Complex Urban Scenarios2 Apr 2025
- 2025A Framework for Responsible AI Systems: Building Societal Trust through Domain Definition, Trustworthy AI Design, Auditability, Accountability, and Governance4 Feb 2025
- 2025From Words to Collisions: LLM-Guided Evaluation and Adversarial Generation of Safety-Critical Driving Scenarios4 Feb 2025