Adaptively Managing Reliability of Machine Learning Perception under Changing Operating Conditions

Salvi, Aniket; Weiss, Gereon; Trapp, Mario

doi:10.1109/seams59076.2023.00019

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2024

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Cited by 3 publications

References 29 publications

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Evaluating Self-Adaptive Architectures for Automated Driving Systems

Sorokos,

Wolf,

Reich

et al. 2024

2024 11th International Conference on Internet of Things: Systems, Management and Security (IOTSMS)

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Evaluating Self-Adaptive Architectures for Automated Driving Systems

Sorokos,

Wolf,

Reich

et al. 2024

2024 11th International Conference on Internet of Things: Systems, Management and Security (IOTSMS)

View full text Add to dashboard Cite

Knowledge-based reliability assessment of models with application to risk stratification

Zhao,

Gil,

Loureiro

et al. 2024

2024 IEEE 22nd Mediterranean Electrotechnical Conference (MELECON)

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Study of Contactless Computer Vision-Based Road Condition Estimation Methods Within the Framework of an Operational Design Domain Monitoring System

Subramanian,

Büker

2024

Eng

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Deployment of Level 3 and Level 4 autonomous vehicles (AVs) in urban environments is significantly constrained by adverse weather conditions, limiting their operation to clear weather due to safety concerns. Ensuring that AVs remain within their designated Operational Design Domain (ODD) is a formidable challenge, making boundary monitoring strategies essential for safe navigation. This study explores the critical role of an ODD monitoring system (OMS) in addressing these challenges. It reviews various methodologies for designing an OMS and presents a comprehensive visualization framework incorporating trigger points for ODD exits. These trigger points serve as essential references for effective OMS design. The study also delves into a specific use case concerning ODD exits: the reduction in road friction due to adverse weather conditions. It emphasizes the importance of contactless computer vision-based methods for road condition estimation (RCE), particularly using vision sensors such as cameras. The study details a timeline of methods involving classical machine learning and deep learning feature extraction techniques, identifying contemporary challenges such as class imbalance, lack of comprehensive datasets, annotation methods, and the scarcity of generalization techniques. Furthermore, it provides a factual comparison of two state-of-the-art RCE datasets. In essence, the study aims to address and explore ODD exits due to weather-induced road conditions, decoding the practical solutions and directions for future research in the realm of AVs.

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Adaptively Managing Reliability of Machine Learning Perception under Changing Operating Conditions

Cited by 3 publications

References 29 publications

Evaluating Self-Adaptive Architectures for Automated Driving Systems

Evaluating Self-Adaptive Architectures for Automated Driving Systems

Knowledge-based reliability assessment of models with application to risk stratification

Study of Contactless Computer Vision-Based Road Condition Estimation Methods Within the Framework of an Operational Design Domain Monitoring System

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