TY - GEN
T1 - Supplemental Taxonomy for SAE L3-L4
T2 - 28th International Conference on Intelligent Transportation Systems, ITSC 2025
AU - Hu, Chengxi
AU - Chen, Sikai
AU - Labi, Samuel
AU - Ding, Hongliang
AU - Chung, Hyungchul
AU - Chen, Tiantian
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025/11
Y1 - 2025/11
N2 - The SAE taxonomy classifies autonomous driving levels based on how driving responsibilities are allocated between human and automated systems. However, it fails to specify critical environmental influences, which result in safety concerns, ambiguous performance expectations, and barriers to commercialization. The purpose of this paper is to fill this gap by introducing a Tri-layer Environmental Grading Framework (TEGF), which is a structured system that evaluates autonomous vehicle adaptability across built, natural, and traffic environments. The TEGF maps environmental favorability against the perception capabilities that are required for safe autonomous operation by quantifying environmental favorability through expert assessments. Our framework focuses specifically on SAE Levels 3 and 4, which we categorize into five tiers of adaptability (A-E). The supplementary classification clarifies operational boundaries and system expectations, thereby guiding technological development, regulatory frameworks, and public understanding of autonomous driving. By incorporating critical environmental dimensions into the SAE taxonomy, TEGF enhances the precision of the SAE taxonomy and provides a solid foundation for developing, testing, and deploying safe and reliable autonomous vehicles.
AB - The SAE taxonomy classifies autonomous driving levels based on how driving responsibilities are allocated between human and automated systems. However, it fails to specify critical environmental influences, which result in safety concerns, ambiguous performance expectations, and barriers to commercialization. The purpose of this paper is to fill this gap by introducing a Tri-layer Environmental Grading Framework (TEGF), which is a structured system that evaluates autonomous vehicle adaptability across built, natural, and traffic environments. The TEGF maps environmental favorability against the perception capabilities that are required for safe autonomous operation by quantifying environmental favorability through expert assessments. Our framework focuses specifically on SAE Levels 3 and 4, which we categorize into five tiers of adaptability (A-E). The supplementary classification clarifies operational boundaries and system expectations, thereby guiding technological development, regulatory frameworks, and public understanding of autonomous driving. By incorporating critical environmental dimensions into the SAE taxonomy, TEGF enhances the precision of the SAE taxonomy and provides a solid foundation for developing, testing, and deploying safe and reliable autonomous vehicles.
KW - automation level
KW - autonomous vehicles
KW - environmental elements
KW - operational design domain
KW - taxonomy
UR - https://www.scopus.com/pages/publications/105036975928
U2 - 10.1109/ITSC60802.2025.11423080
DO - 10.1109/ITSC60802.2025.11423080
M3 - Conference Proceeding
AN - SCOPUS:105036975928
T3 - IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC
SP - 2727
EP - 2733
BT - IEEE Intelligent Transportation Systems Conference, ITSC 2025
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 18 November 2025 through 21 November 2025
ER -