Identification and Quantification of Hazardous Scenarios for Automated Driving
DOI: 10.1007/978-3-030-58920-2_11
archive: archived pipeline: cataloged verified
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Summary
This document outlines the methodological framework for identifying and quantifying hazardous scenarios associated with Highly Automated Driving (HAD) at Level 3 (L3). The research is motivated by the shift in accident causation dynamics introduced by automation. While conventional accidents are primarily caused by human factors, the increasing penetration of automated vehicles aims to reduce these incidents. However, automation introduces new risks, such as situations where the system is overwhelmed or fails to handle scenarios that human drivers could manage, potentially leading to consumer acceptance issues. The core objective is to define which automation risks exist, how they emerge, and how they can be systematically identified and quantified within the PEGASUS Method. The approach categorizes accident causation into two main types: those prevented by automation and those raised by automation. The latter is analyzed through three distinct classes: (1) impacts of the environment on the automation, such as misguided perception or prediction; (2) impacts of the automation on other traffic participants, including misinterpretation of automation behavior or social acceptance issues; and (3) interactions between the driver, automation, and environment, covering mode confusion, loss of confidence, and misuse of functions. The methodology employs a structured analysis involving function and environment models to identify cause-effect chains for hazards. This process includes fault injection and the analysis of latent and active failures that cause holes in the "Layers of Defense." The framework checks whether conditions can be quantified and if environment conditions are realistic, leading to the derivation of functional and interaction scenarios. The findings present a comprehensive workflow for risk assessment. By refining function and environment models, the method generates scenarios with specific parameter ranges and associated risks. These risks are evaluated based on probability, severity, and controllability to determine the Automotive Safety Integrity Level (ASIL) and residual risk. The analysis considers various sources, including the driver, other road users, vehicle systems, traffic rules, and infrastructure. The output of this process is a test specification database that supports the validation of automated driving systems. The document emphasizes that while automation prevents traditional human-error accidents, it requires rigorous identification of new failure modes, particularly those involving the interaction between the human driver and the automated system during takeover requests or system limitations. The significance of this work lies in its contribution to road safety standards for automated vehicles. By providing a structured way to quantify automation-specific risks, the framework helps ensure that HAD systems contribute positively to safety within their operational limits. It addresses the critical need to understand and mitigate new accident causations that arise from the complex interplay between automation, environment, and human drivers, thereby supporting the development of robust safety requirements and testing protocols for L3 automated driving.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-18 |
| archive | success | canonical_url | — | — | 1 | 2026-06-25 |
| extract | success | pdftotext | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-26 |
| chunk | success | chunk | — | — | 1 | 2026-06-26 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-26 |
| enrich | failed | — | — | — | 4 | 2026-06-26 |
| promote | success | — | — | — | 1 | 2026-06-18 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-26 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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Information type
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- Empirical Findings: crash risk outcomes
- Theoretical Contribution: conceptual framework, computational model