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 a 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.

翻译：端到端自动驾驶通过直接从传感器输入预测未来轨迹，绕过了传统的模块化流水线，近期取得了显著进展。然而，现有方法往往忽略自我车辆规划中的因果相互依赖关系，忽视了自我车辆与周围智能体之间的交互关联。这种因果缺失会导致轨迹预测不一致且不可靠，尤其在需要联合推理自我决策与邻近智能体行为的交互关键场景中。为解决这一局限，我们提出CaAD——一种因果感知的端到端自动驾驶框架，该框架在共享的隐式场景表征中捕捉这些依赖关系。首先，我们设计了基于自我中心的联合因果建模模块，该模块构建于边际预测分支之上，学习自我车辆与交互相关智能体之间的因果依赖关系。其次，我们采用基于联合模式嵌入实现的因果感知策略对齐阶段，将随机自我策略与从周围交通及地图上下文计算得到的规划导向闭环反馈对齐。在Bench2Drive和NAVSIM基准测试中，CaAD展现了出色的闭环规划性能，在Bench2Drive上达到87.53的驾驶分数和71.81的成功率，在NAVSIM上获得91.1的PDMS值。