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Found 5 papers, 0 papers with code
Explainable AI for Safe and Trustworthy Autonomous Driving: A Systematic Review
One way to mitigate this challenge is to utilize explainable AI (XAI) techniques.
SURE-Val: Safe Urban Relevance Extension and Validation
Therefore, this work adopts an existing method to define relevance in the highway domain and expands it to the urban domain.
Towards Establishing Systematic Classification Requirements for Automated Driving
Despite the presence of the classification task in many different benchmark datasets for perception in the automotive domain, few efforts have been undertaken to define consistent classification requirements.
Conservative Estimation of Perception Relevance of Dynamic Objects for Safe Trajectories in Automotive Scenarios
Finally, a possibility for a future validation of the presented relevance concept is outlined.
Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology
The sixth phase covers state-of-the-art approaches for monitoring and maintenance of a machine learning applications, as the risk of model degradation in a changing environment is eminent.
