A Taxonomy for Autonomous Vehicles Considering Ambient Road Infrastructure
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Summary
This paper addresses a critical gap in the Society of Automotive Engineers (SAE) taxonomy for autonomous vehicles, which currently defines automation levels solely based on vehicle system capabilities without accounting for the necessary ambient road infrastructure. The authors argue that the operational design domain (ODD) of an automated driving system is inextricably linked to infrastructure support, such as lane markings, traffic signals, and vehicle-to-infrastructure (V2I) connectivity. Because the current SAE Level 4 classification fails to distinguish between vehicles operating in highly controlled environments versus complex urban settings, the paper proposes a supplemental taxonomy to clarify performance expectations and guide infrastructure planning. The study focuses specifically on Level 4 automation, identified as the most imminent level for widespread deployment. The authors analyzed driving scenarios and state-of-the-art infrastructure technologies to categorize environments based on their structural complexity and technological "smartness." They defined five distinct infrastructure types: Dedicated Guideways (exclusive, fully controlled lanes with intelligent infrastructure); Expressways (reliable V2I/V2V communication, unidirectional traffic); Well-Structured Roads (clear markings and signals, mixed traffic); Limited-Structured Roads (incomplete markings, potential environmental obstructions like ice or dirt); and Disorganized Areas (crowded, limited space, no intelligent infrastructure support). Based on this analysis, the authors propose five sub-levels for Level 4 automation: Level 4-A (Dedicated Guideway), Level 4-B (Expressway), Level 4-C (Well-Structured Road), Level 4-D (Limited-Structured Road), and Level 4-E (Disorganized Area). These sub-levels reflect a progression from environments with robust infrastructure support requiring minimal vehicle perception capabilities, to unstructured environments requiring advanced real-time object detection, navigation, and collision avoidance. For instance, a vehicle capable of Level 4-A automation in a tunnel may lack the necessary sensors to operate safely at Level 4-C in mixed urban traffic. The significance of this proposed supplement lies in its potential to standardize definitions for manufacturers, consumers, and policymakers. By explicitly linking automation levels to infrastructure requirements, the taxonomy helps manufacturers accurately delineate vehicle capabilities, preventing misleading marketing claims. It enables consumers to understand the specific environments in which their vehicles can operate safely. Furthermore, it provides transportation agencies with a framework for planning infrastructure investments and regulatory policies, ensuring that road networks are adequately prepared to support the varying demands of emerging autonomous vehicle technologies.
Key finding
The study proposes a five-tier supplemental taxonomy for SAE Level 4 autonomous vehicles that categorizes operational capabilities based on the specific type and sophistication of ambient road infrastructure.
Methodology
theoretical
Provenance
The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).
| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | rosap | — | — | 2 | 2026-05-23 |
| archive | success | — | — | — | 1 | 2026-05-23 |
| extract | success | cached | — | — | 2 | 2026-06-10 |
| clean | success | — | — | — | 1 | 2026-06-01 |
| chunk | success | — | — | — | 1 | 2026-06-01 |
| embed | success | — | — | — | 1 | 2026-06-02 |
| enrich | success | — | — | — | 1 | 2026-05-23 |
| promote | success | — | — | — | 1 | 2026-05-23 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 3 | 2026-06-10 |
| tag | success | vector_similarity | — | — | 19 | 2026-06-11 |
| verify | success | — | — | — | 2 | 2026-06-10 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.
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- Theoretical Contribution: conceptual framework