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
The increasing deployment of AI in safety-critical applications like autonomous driving necessitates the development of Trustworthy AI due to mounting regulatory pressure and societal demand for transparency.
Developing trustworthy and explainable AI for perception models is crucial for unlocking broader adoption of autonomous systems, ensuring regulatory compliance, and building public confidence in AI technology.
The explicit integration of explainability and robustness into perception modules marks a shift from solely performance-driven AI development to a more holistic approach that considers safety, auditing, and human oversight.
- · Autonomous Vehicle Developers
- · AI Safety & Ethics Researchers
- · Regulatory Bodies
- · Specialized AI Software Companies
- · AI Developers focused solely on 'black box' performance
- · Companies unable to meet explainability standards
Wider deployment of autonomous driving technologies becomes feasible due to enhanced safety and auditability.
Increased consumer trust and reduced liability risks accelerate the market growth of AI-powered systems beyond automotive.
New certification and auditing industries emerge specifically for Trustworthy and Explainable AI, reshaping regulatory landscapes globally.
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Read at arXiv cs.AI