Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment

arXiv:2605.16087v2 Announce Type: replace-cross Abstract: Deep Neural Networks have become the dominant solution for Autonomous Driving perception, but their opacity conflicts with emerging Trustworthy AI guidelines and complicates safety assurance, debugging, and human oversight. While theoretical frameworks for safe and Explainable AI (XAI) exist, concrete implementations of Trustworthy AI for 3D scene understanding remain scarce. We address this gap by proposing a Trustworthy AI perception module that is remarkably robust, integrates faithful explainability, and calibrated uncertainty estim

Source: arXiv cs.AI — read the full report at the original publisher.

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