Gauging requirements and testing models for Space, Automotive and Railway Case Studies
Using three case studies from different industrial domains ensures that the project considers the needs of multiple fields whose common thread is the potential use of autonomous systems in complex environments, where AI can enable critical and powerful features.
COMPSAC 23: Presenting acceleration solutions based on Deep Neural Networks (DNNs) for use in safety-critical systems
BSC researcher Martí Caro presented “Efficient Diverse Redundant DNNs for Autonomous Driving” on 27 June 2023 at the Autonomous Systems Symposium (ASYS) within the 47th IEEE International Conference on Computers, Software & Applications (COMPSAC). The theme of...
Integrating Explainable AI techniques into Machine Learning Life Cycles
Written by Robert Lowe & Thanh Bui, Humanized Autonomy Unit, RISE, Sweden. Machine Learning life cycles for data science projects that deal with safety critical outcomes require assurances of expected outputs at each stage of the life cycle for them to be...
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