Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment
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 estimates. Building on a transformer-based detector, we derive explanation from the attention mechanism at inference time and validate their faithfulness using ...
| Authors | Till Beemelmanns, Shayan Sharifi, Manas Mehrotra et al. |
|---|---|
| Primary category | cs.RO |
| Published | 15 May 2026 |
| Last updated at source | 22 May 2026 |
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