CausalVAD: De-confounding End-to-End Autonomous Driving via Causal Intervention
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 confusion, where models exploit dataset biases as shortcuts, critically harming their reliability and safety in complex scenarios. To address this, we introduce CausalVAD, a de-confounding training framework that leverages causal intervention. At its core, we design the sparse causal intervention scheme (SCIS), a lightweight, plug-and-play module to instantiate the backdoor adjustment theory in neural networks. SCIS constructs a dictionary of prototypes representing latent driving contexts. It then us...
| Authors | Jiacheng Tang, Zhiyuan Zhou, Zhuolin He et al. |
|---|---|
| Primary category | cs.CV |
| Published | 19 March 2026 |
| Last updated at source | 10 April 2026 |
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