End-to-End autonomous driving (E2E-AD) systems face challenges in lifelong learning, including catastrophic forgetting, difficulty in knowledge transfer across diverse scenarios, and spurious correlations between unobservable confounders and true driving intents. To address these issues, we propose DeLL, a Deconfounded Lifelong Learning framework that integrates a Dirichlet process mixture model (DPMM) with the front-door adjustment mechanism from causal inference. The DPMM is employed to construct two dynamic knowledge spaces: a trajectory knowledge space for clustering explicit driving behaviors and an implicit feature knowledge space for discovering latent driving abilities. Leveraging the non-parametric Bayesian nature of DPMM, our framework enables adaptive expansion and incremental updating of knowledge without predefining the number of clusters, thereby mitigating catastrophic forgetting. Meanwhile, the front-door adjustment mechanism utilizes the DPMM-derived knowledge as valid mediators to deconfound spurious correlations, such as those induced by sensor noise or environmental changes, and enhances the causal expressiveness of the learned representations. Additionally, we introduce an evolutionary trajectory decoder that enables non-autoregressive planning. To evaluate the lifelong learning performance of E2E-AD, we propose new evaluation protocols and metrics based on Bench2Drive. Extensive evaluations in the closed-loop CARLA simulator demonstrate that our framework significantly improves adaptability to new driving scenarios and overall driving performance, while effectively retaining previous acquired knowledge.

翻译：端到端自动驾驶系统在持续学习中面临诸多挑战，包括灾难性遗忘、跨场景知识迁移困难，以及不可观测混杂因素与真实驾驶意图之间的虚假关联。为解决这些问题，我们提出DeLL——一种去混杂持续学习框架，该框架将狄利克雷过程混合模型与因果推断中的前门调整机制相结合。DPMM用于构建两个动态知识空间：用于聚类显式驾驶行为的轨迹知识空间，以及用于发现潜在驾驶能力隐式特征知识空间。利用DPMM的非参数贝叶斯特性，我们的框架无需预设聚类数量即可实现知识的自适应扩展与增量更新，从而缓解灾难性遗忘。同时，前门调整机制将DPMM推导的知识作为有效中介变量，用于消除传感器噪声或环境变化等导致的虚假关联，并增强所学表征的因果表达能力。此外，我们引入进化轨迹解码器实现非自回归规划。为评估端到端自动驾驶的持续学习性能，我们基于Bench2Drive提出新的评估协议与指标。在闭环CARLA模拟器上的大量实验表明，该框架显著提升了新驾驶场景的适应能力与整体驾驶性能，同时有效保留了先前习得的知识。