CausalVAD: De-confounding End-to-End Autonomous Driving via Causal Intervention
arXiv:2603.18561v2 Announce Type: replace-cross
Abstract: Planning-oriented end-to-end driving models show great promise, yet they fundamentally learn statistical correlations instead of true causal relationships. This vulnerability leads to causal co…