A new Taxonomy for Automated Driving: Structuring Applications based on their Operational Design Domain, Level of Automation and Automation Readiness
Johannes Betz, Melina Lutwitzi, Steven Peters
TL;DR
The paper addresses the challenge of comparing automated driving systems across diverse ODDs, SAE Levels, and technology maturities. It proposes an integrated taxonomy that augments SAE Levels with an intermediate ODD layer and a NASA-inspired Automated Driving-Readiness Level (ADRL) to quantify maturity. The methodology comprises three steps: (1) structure ODDs at an intermediate abstraction level, (2) attach SAE Levels to the ODD taxonomy, and (3) estimate readiness via ADRL. The authors apply the framework to representative ADS cases (truck highway pilot, automated valet parking, highway pilot, robotaxi, mining trucks) to illustrate differentiations and reveal regulatory and-market gaps. The framework aims to standardize comparisons, support regulatory discussions, and guide research by making differences in ODD, automation levels, and readiness explicit.
Abstract
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.