Safety is one of the key aspects of road vehicles. With applications of machine learning and artificial intelligence (AI) technologies, driver assistance and automated driving systems have been rapidly developed. This paper identifies one of the emerging safety issues of automated driving systems: functional deficiencies resulting from limited sensing abilities and algorithmic performance. Safety validation problem and challenges for some methodologies provided by ISO 26262 are addressed. To this end, we provide a methodology framework for identifying functional deficiencies during system development. A novel methodology based on possibility theory and a fuzzy relation model, Causal Scenario Analysis (CSA), is introduced as one essential part in this framework. A traffic light handling case study is presented.
Taming Functional Deficiencies of Automated Driving Systems: a Methodology Framework toward Safety Validation
2018 IEEE Intelligent Vehicles Symposium (IV) ; 1918-1924
2018-06-01
716855 byte
Conference paper
Electronic Resource
English
British Library Conference Proceedings | 2018
|Causal modeling and reasoning for identifying functional deficiencies of automated driving systems
TIBKAT | 2021
|Toward a Framework for Highly Automated Vehicle Safety Validation
British Library Conference Proceedings | 2018
|Toward a Framework for Highly Automated Vehicle Safety Validation
SAE Technical Papers | 2018
|