Causality-Aware End-to-End Autonomous Driving via Ego-Centric Joint Scene Modeling

• URL: http://arxiv.org/abs/2605.13646v2
• Date: Tue, 19 May 2026 11:58:26 GMT
• Title: Causality-Aware End-to-End Autonomous Driving via Ego-Centric Joint Scene Modeling
• Abstract summary: We propose a Causality-aware end-to-end Autonomous Driving framework called CaAD.<n>First, we propose an ego-centric joint-causal modeling module that builds on the marginal prediction branch.<n>Second, we employ a causality-aware policy alignment stage implemented with joint-mode embeddings to align the ego policy with planning-oriented closed-loop feedback.
• Score: 20.79524129066014
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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 dependencies within a shared latent scene representation. First, we propose an ego-centric joint-causal modeling module that builds on the marginal prediction branch, and learns causal dependencies between the ego vehicle and interaction-relevant agents. Second, we employ a causality-aware policy alignment stage implemented with joint-mode embeddings to align the stochastic ego policy with planning-oriented closed-loop feedback computed from surrounding traffic and map context. On the Bench2Drive and NAVSIM benchmarks, CaAD demonstrates strong closed-loop planning performance, achieving a Driving Score of 87.53 and Success Rate of 71.81 on Bench2Drive, and a PDMS of 91.1 on NAVSIM. The project page is available at https://moonseokha.github.io/CaAD/.

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