Specifying Operational Design Domain in Autonomous Driving for Comprehensive Data Evaluation
Operational Design Domain (ODD) attributes define the environmental and infrastructural conditions under which Automated Driving Systems (ADS) can safely operate. These attributes include factors such as road and lighting conditions, as well as infrastructure elements like lane markings and road conditions. However, existing ODD definitions are often ambiguous and lack specificity, making it challenging to validate their presence in datasets. The absence of precise ODD definitions and robust validation mechanisms poses significant challenges, as it remains unclear whether ADS training and testing datasets adequately represent real-world operating conditions. This gap introduces risks that could compromise the safe deployment of ADS in diverse environments. To address this issue, we introduce FODSE (Framing ODDs as Domain Specifications for Evaluation), a semi-automated AI-powered approach that refines ODD attributes into structured, context-aware domain specifications and systematically evaluates their presence in datasets. FODSE leverages Retrieval-Augmented Generation (RAG), multimodal AI, and prompt learning to enhance specification clarity and dataset completeness. Experimental evaluation on two commonly adopted datasets in ADS demonstrates that FODSE significantly improves dataset validation accuracy, achieving up to 96.8% classification accuracy for an extended set of lane marking variants and 97.8% for roadway users—two key ODD attributes. Expert assessments confirm that FODSE effectively reduces ambiguity and enhances contextual adaptability, reinforcing its potential to improve dataset integrity and ensure safer, more reliable ADS training and validation.
Fri 5 SepDisplayed time zone: Brussels, Copenhagen, Madrid, Paris change
14:00 - 15:20 | Safety-critical SystemsIndustrial Innovation Track / Research Papers at Salon de Actos Chair(s): Stefania Gnesi Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" | ||
14:00 30mPaper | Taxonomy-Guided Reasoning for Requirements Classification: A Study in Aerospace Industry Industrial Innovation Track Yixing Luo Beijing Institute of Control Engineering, Yang Liu Beijing Institute of Control Engineering, Xiaofeng Li Beijing Institute of Control Engineering, Xiaogang Dong Beijing Institute of Control Engineering, Bin Gu Beijing Institute of Control Engineering, Zhi Jin Peking University, Mengfei Yang China Academy of Space Technology | ||
14:30 30mPaper | Specifying Operational Design Domain in Autonomous Driving for Comprehensive Data Evaluation Research Papers Hamed Barzamini , Ramesh S , Arun Adiththan General Motors, Prakash Peranandam General Motors, Mona Rahimi Northern Illinois University | ||
15:00 20mPaper | Requirements Dependency Driven Test Case Generation: An Automotive Industry Practice Industrial Innovation Track Tong Xu , Zheng Zhou , Xiaohong Chen , Zhiyi Xue , Yi Zhao State Key Laboratory for Novel Software Technology, Nanjing University, Min Zhang East China Normal University, Zhi Jin Peking University |