WMKD: Planning-Oriented World Model via Future-Guided Knowledge Distillation for End-to-End Autonomous Driving

End-to-end autonomous driving requires anticipating how the surrounding scene will evolve to make safe and effective planning decisions. World models provide this context by learning to predict future scene evolution, yet a fundamental question remains: "What should a world model predict for planning?" Existing approaches largely formulate world modeling as reconstructing future observations or scene representations, often without explicitly modeling how these targets should be constructed to support the current planning decision. To this end, we present World Model Knowledge Distillation (WMKD), a teacher–student framework in which a future-guided teacher constructs a planning-oriented prediction target and a student learns to predict it from sensor observations. The teacher encodes ground-truth future semantic evolution under joint supervision from future semantic reconstruction and ego planning, and quantizes the resulting scene representation into discrete codes. The student's Discrete World Model (DWM) predicts these codes to anticipate planning-relevant future knowledge without privileged inputs at inference. To incorporate the predicted representation into planning, the Residual Fusion Module (RFM) fuses it with the observed BEV and their residual, while Planning Response Distillation (PRD) transfers the teacher's planning responses. The Trajectory Repropagation Planner (TRP) further re-encodes refined trajectory geometry to guide successive refinement. Extensive experiments across NAVSIM and Bench2Drive validate WMKD, achieving 92.0 PDMS and 90.6 EPDMS on NAVSIM, as well as a Driving Score (DS) of 87.56 and a Success Rate (SR) of 72.27% on Bench2Drive.