Deconfounded Lifelong Learning for Autonomous Driving via Dynamic Knowledge Spaces

End-to-End autonomous driving (E2E-AD) systems face chal-lenges in lifelong learning, including catastrophic forgetting, difficulty inknowledge transfer across diverse scenarios, and spurious correlationsbetween unobservable confounders and true driving intents. To addressthese issues, we propose DeLL, a Deconfounded Lifelong Learning frame-work that integrates a Dirichlet process mixture model (DPMM) withthe front-door adjustment mechanism from causal inference. The DPMMis employed to construct two dynamic knowledge spaces: a trajectoryknowledge space for clustering explicit driving behaviors and an implicitfeature knowledge space for discovering latent driving abilities. Leverag-ing the non-parametric Bayesian nature of DPMM, our framework en-ables adaptive expansion and incremental updating of knowledge withoutpredefining the number of clusters, thereby mitigating catastrophic for-getting. Meanwhile, the front-door adjustment mechanism utilizes theDPMM-derived knowledge as mediators to deconfound spurious correla-tions, 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 en-ables non-autoregressive planning. To evaluate the lifelong learning per-formance of E2E-AD, we propose new evaluation protocols and metricsbased on Bench2Drive. Extensive evaluations in the closed-loop CARLAsimulator demonstrate that our framework significantly improves adapt-ability to new driving scenarios and overall driving performance, whileeffectively retaining previously acquired knowledge.