Identification of Potential Hazardous Events for an Unmanned Protective Vehicle
Gerrit Bagschik, Andreas Reschka, Torben Stolte, Markus Maurer

TL;DR
This paper presents a novel method combining skill graphs and scene definitions to systematically identify hazardous events in automated unmanned protective vehicles, enhancing safety analysis for complex driving scenarios.
Contribution
It introduces a new hazard identification approach tailored for automated vehicles using functional models and scene analysis, addressing limitations of existing standards.
Findings
Method successfully identified potential hazardous events in a real use case
Enhanced hazard analysis for complex automated driving scenarios
Supports systematic safety assessment in autonomous vehicle development
Abstract
The project Automated Unmanned Protective Vehicle for Highway Hard Shoulder Road Works (aFAS) aims to develop an unmanned protective vehicle to reduce the risk of injuries due to crashes for road workers. To ensure functional safety during operation in public traffic the system shall be developed following the ISO 26262 standard. After defining the functional range in the item definition, a hazard analysis and risk assessment has to be done. The ISO 26262 standard gives hints how to process this step and demands a systematic way to identify system hazards. Best practice standards provide systematic ways for hazard identification, but lack applicability for automated vehicles due to the high variety and number of different driving situations even with a reduced functional range. This contribution proposes a new method to identify hazardous events for a system with a given functional…
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