Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models
The increasing adoption of end-to-end learning for autonomous driving introduces increased model complexity and opacity, raising the risk of learning undesired or erroneous behavior. In this work, we integrate unsupervised dictionary learning as a post hoc interpretability module within state-of-the-art driving models to decompose driving behavior into semantically meaningful concepts while demonstrating their causal influence on the model's driving decisions. We propose a stepwise framework for extracting and interpreting meaningful concepts from the end-to-end model and connecting them to th
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- PossiblePossibly related (embedding) · 53%autowarefoundation/auto_e2e →
- LinkedLinked via arxiv author · 85%Franz Motzkus →
“Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models”
- LinkedLinked via arxiv author · 85%Sebastian Bernhard →
“Driving the Wrong Way: Leveraging Interpretability in End2End Autonomous Driving Models”