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Runtime Active Safety Risk-Assessment of Highly Autonomous Vehicles for Safe Nominal Behavior
Technical Paper
2020-01-0107
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Fatal crashes involving automated driving systems, has been raising the concern of minimum standard requirement for safety, reliability and performance required for Autonomous Driving System (ADS)/Advanced Driver Assistance System (ADAS) before this cutting-edge technology takes on public roads. Hence, in order to ensure necessary safety requirements of ADS/ADAS systems we propose a runtime active safety assurance module known as SConSert. SConSert performs dynamic risk assessment of “Sensing, Planning and Action module of ADS/ADAS”; to provide minimal risk maneuver in any given driving scenario. The dynamic risk assessment of ADS/ADAS system is based on the operational design domain (ODD) knowledge of the driving scenario plus the sensor capability, ADS/ADAS algorithm requirement and capability, and finally smooth and collision free maneuver requirement. So, the main concept behind SConSert is runtime derivation of situational and conditional set of contracts for a given driving scenario and ADS/ADAS system ODD; fulfillment or violation of which can help in runtime dynamic risk assessment of ADS/ADAS to plan minimal safe behavior such that necessary safety requirements can be achieved. Finally, through experiment we show that proposed runtime active assurance safety module can handle complex driving scenario, and present simulation and experimental results that emphasizes the importance of the proposed runtime safety assurance module and shows that the proposed system is capable of performing runtime dynamic risk assessment in order to keep the automated driving systems always within the safe sate that is the automated driving system always perform within its ODD.
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Authors
Topic
Citation
Rathour, S., Ishigooka, T., Otsuka, S., and MARTIN, R., "Runtime Active Safety Risk-Assessment of Highly Autonomous Vehicles for Safe Nominal Behavior," SAE Technical Paper 2020-01-0107, 2020, https://doi.org/10.4271/2020-01-0107.Also In
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