How Many Futures Are Enough? Redundancy-Aware Effective Multimodality for End-to-End Autonomous Driving

Multimodal end-to-end planners can generate multiple plausible trajectories for the same driving scene, yet a larger set of candidates does not necessarily represent a larger set of distinct futures. Numerically different trajectories may repeatedly describe closely related driving decisions and induce similar future evolution, resulting in multimodal redundancy. We introduce effective multimodality to distinguish the nominal number of generated trajectories from the effective future structure represented by the candidate set. We propose AMC-Drive (Action-conditioned Multimodal Consolidation for Driving), which uses a frozen action-conditioned predictive model to establish candidate-level relations beyond trajectory geometry. AMC-Drive reshapes redundant candidate structure through effective-multimodality calibration and topology-aware redundancy correction, while a cross-mode guard preserves distinct decision modes. The physical output cardinality remains unchanged throughout this process. On NAVSIM v1, AMC-Drive achieves 93.9 PDMS, establishing a new state of the art among JEPA-based world-model planners. It further reaches 89.2 EPDMS on NAVSIM v2 and obtains a 0.4308 HD-Score on HUGSIM without HUGSIM-specific fine-tuning. AMC further yields consistent gains across proposal-based, diffusion-based, and flow-matching planners, and transfers to robotic manipulation, demonstrating the broader applicability of redundancy-aware effective multimodality across multimodal decision systems.