Causality-Aware End-to-End Autonomous Driving via Ego-Centric Joint Scene Modeling
End-to-end autonomous driving, which bypasses traditional modular pipelines by directly predicting future trajectories from sensor inputs, has recently achieved substantial progress. However, existing methods often overlook the causal inter-dependencies in ego-vehicle planning, ignoring the reciprocal relations between the ego vehicle and surrounding agents. This causal oversight leads to inconsistent and unreliable trajectory predictions, especially in interaction-critical scenarios where ego decisions and neighboring agent behaviors must be reasoned about jointly. To address this limitation, we propose CaAD, a Causality-aware end-to-end Autonomous Driving framework that captures these d...
| Authors | Seokha Moon, Minseung Lee, Joon Seo et al. |
|---|---|
| Primary category | cs.RO |
| Published | 13 May 2026 |
| Last updated at source | 19 May 2026 |
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