Barcelona, 18 October 2023—The newly released video from the EU-funded SAFEXPLAIN project explains how it tackles safety and explainability challenges associated with the use of deep learning (DL) solutions in critical autonomous systems, like cars, trains and satellites.
Partner Ikerlan is pursuing the development of two safety functions that will minimize the risk associated with Automatic Train Operation. Two scenarios offer approaches for minimizing the risk of a train running over or injuring people on the track as well as avoiding damages during opening/closing operations on the platform. Four activities are underway to support these scenarios.
The challenge faced by the railway case study is closing the gap between Functional Safety Requirements and the nature of Deep Learning (DL) solutions. Functional Safety systems need deterministic, verifiable and pass/fail test-based software solutions.
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.
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