CoReDrive: Actor-Conditioned Planning with Counterfactual Response Learning

Autonomous driving requires anticipating the behavior of other road users and adapting ego plans accordingly, but driving logs provide supervision only for observed interactions. We present CoReDrive, which couples actor perception, multimodal motion prediction, motion-derived intent, and ego planning through a structured actor interface. To teach responses beyond the logged interaction, we edit an actor's predicted futures while holding the observation and current scene geometry fixed. A geometric teacher constructs sets of admissible ego responses instead of treating a synthetic trajectory as a unique expert. Response-set targets supervise candidate retention and ranking, while paired factual and edited worlds supervise an interaction-score correction used during velocity admission and final selection. On NAVSIM, CoReDrive achieves 90.8% and 91.4% corrected extended Predictive Driver Model Score with ResNet-34 and ViT-L, respectively. A matched ResNet-34 comparison shows a gain from counterfactual response supervision. Controlled actor-information and future-edit visualizations examine how the planner uses its interaction interface in selected scenes.