Tag: SMILE
To increase the level of autonomy, future vehicles will depend on functions that rely on Deep machine learning (DML) whose correct behavior cannot be guaranteed by traditional software engineering approaches. Furthermore, crucial parts in ISO 26262 are not well defined for addressing autonomous systems, and certain process requirements and recommendations are not applicable for the development of machine learning in the domains of specification, design, and testing. The SMILE II project focuses on developing methods that allow DML-based functions to be included into safety critical vehicular applications.
Digital Twins Are Not Monozygotic – Cross-Replicating ADAS Testing in Two Industry-Grade Automotive Simulators
Requirements Engineering for Machine Learning: Perspectives from Data Scientists
Performance Analysis of Out-of-Distribution Detection on Various Trained Neural Networks
Towards Structured Evaluation of Deep Neural Network Supervisors
Safely Entering the Deep: A Review of Verification and Validation for Machine Learning and a Challenge Elicitation in the Automotive Industry
