I. Bloise

TL;DR: In this article , an Object Detection Convolutional Neural Network (ODN) is used to extract features of the observed craters that are then processed by standard image processing algorithms in order to provide pseudo-measurements that can be used by navigation filter.

TL;DR: A deep Recurrent Neural Network architecture capable of predicting the fuel-optimal thrust from sequence of states during a powered planetary descent is designed, test and validate and the principle behind imitation learning (supervised learning) are applied.

TL;DR: The SAFEXPLAIN project as mentioned in this paper proposes a flexible approach to certify DL-based solutions in critical autonomous AI-based systems (CAIS) building on DL solutions that provide end-to-end traceability, with specific approaches to explain whether predictions can be trusted and strategies to reach (and prove) correct operation, in accordance to certification standards.

TL;DR: SAFEXPLAIN, a 3-year Horizon Europe project, addresses the challenge of designing trustworthy AI-based safety-critical systems by providing a comprehensive approach, concepts, processes, tools, and frameworks to adhere to functional safety standards in automotive, space, and railway domains.