arXiv:2411.18714v3 Announce Type: replace-cross Abstract: Self-driving cars increasingly rely on deep neural networks to achieve human-like driving. The opacity of such black-box planners makes it challenging to accurately anticipate when they will fail, with potentially catastrophic consequences. While research into interpreting these systems has surged, most of it is confined to simulations or toy setups due to the difficulty of real-world deployment, leaving the practical utility of such techniques unknown. Here, we introduce the Concept-Wrapper Network (CW-Net), a method for faithfully exp
The increasing reliance on deep neural networks in critical applications like self-driving cars necessitates advancements in explainability to address black-box opacity and safety concerns.
Improved explainability in deep learning for autonomous systems can accelerate deployment, enhance safety, and build public trust, moving these technologies from simulations to real-world utility.
The introduction of techniques like Concept-Wrapper Networks offers a path to more transparent and reliable AI decision-making in autonomous vehicles, directly impacting their real-world applicability.
- · autonomous vehicle manufacturers
- · AI safety researchers
- · regulatory bodies
- · consumers of autonomous services
- · black-box AI developers without explainability features
- · companies relying solely on simulation-based validation
Explainable AI reduces safety risks and accelerates the adoption of autonomous driving technologies.
Public confidence in AI-driven systems increases, leading to broader integration of AI into other critical infrastructure.
The demand for explainable AI frameworks extends beyond autonomous vehicles to other AI-driven sectors, potentially becoming a standard for deployment.
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Read at arXiv cs.AI