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Review | Open Access | Just Accepted

Do SAE levels reflect real-world capability? A supplemental environmental-situational-capability taxonomy for L3–L4 automated driving

Chengxi Hu1, Sikai Chen2( ), Samuel Labi3, Hongliang Ding4, Hyungchul Chung5, Tiantian Chen1( )

1 Cho Chun Shik Graduate School of Mobility, Korea Advanced Institute of Science and Technology, Daejeon 34051, Republic of Korea.

2 Department of Civil and Environmental Engineering, University of Wisconsin–Madison, Madison 53706, USA.

3 Lyles School of Civil and Construction Engineering, Purdue University, West Lafayette 47907, USA.

4 Institute of Smart City and Intelligent Transportation, Southwest Jiaotong University, Chengdu 611756, China.

5 Department of Urban Planning and Design, Xi'an Jiaotong- Liverpool University, Suzhou 215123, China.

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Abstract

The widely adopted SAE (Society of Automotive Engineers) taxonomy categorizes driving automation primarily based on the division of operational responsibilities between the human driver and the system. This has worked well for several applications. However, it fails to distinguish the requisite versatility of autonomous vehicle (AV) performance across diverse operational environments. This structural limitation has spawned critical challenges, including safety risks, commercialization barriers, regulation challenges, and inadequate Operational Design Domain (ODD) development guidelines toward Level 5 autonomy. To address these gaps, this paper proposes a supplemental taxonomy for SAE Levels 3 and 4 using an Environmental-Situational Capability (ESC) evaluation framework across three core dimensions: the built environment, natural environment, and ambient traffic conditions. Using a two-stage hierarchical classification strategy, the ESC framework maps expert evaluations of a specific automated driving system (ADS) to corresponding levels of the proposed taxonomy. This novel classification bridges the ADS level and the ODD by correlating environmental complexity with the minimum capabilities required for safe autonomous operation. Operating orthogonally to SAE standards, this taxonomy categorizes systems based on their actual operational capabilities rather than driving automation task responsibility alone. Consequently, it establishes a crucial alignment across researchers, manufacturers, regulators, and consumers, providing a practical reference for product benchmarking, safety validation, and policymaking.

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Journal of Intelligent and Connected Vehicles

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Cite this article:
Hu C, Chen S, Labi S, et al. Do SAE levels reflect real-world capability? A supplemental environmental-situational-capability taxonomy for L3–L4 automated driving. Journal of Intelligent and Connected Vehicles, 2026, https://doi.org/10.26599/JICV.2026.9210100

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Received: 14 May 2026
Revised: 07 August 2026
Accepted: 14 September 2026
Available online: 08 October 2026

© The Author(s) 2026.

This is an open access article under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0, http://creativecommons.org/licenses/by/4.0/).