A structured perturbation framework applied to VLA driving models reveals evaluation-dependent visual grounding patterns and uneven dependency across abstraction levels.
Decoupling scene perception and ego sta- tus: A multi-context fusion approach for enhanced gener- alization in end-to-end autonomous driving
2 Pith papers cite this work. Polarity classification is still indexing.
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CausalVAD applies sparse causal intervention to remove spurious correlations from end-to-end autonomous driving models, reporting state-of-the-art planning accuracy and robustness on nuScenes.
citing papers explorer
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Does Visual Information Play a Decisive Role in Vision-Language-Action Model Driving Behavior?
A structured perturbation framework applied to VLA driving models reveals evaluation-dependent visual grounding patterns and uneven dependency across abstraction levels.
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CausalVAD: De-confounding End-to-End Autonomous Driving via Causal Intervention
CausalVAD applies sparse causal intervention to remove spurious correlations from end-to-end autonomous driving models, reporting state-of-the-art planning accuracy and robustness on nuScenes.