BOLT: Behavior-Oriented Learning for Autonomous Driving Decision-Making

Autonomous driving in mixed traffic requires understanding surrounding drivers' behavioral tendencies and how they affect ego decisions. However, trajectory supervision alone does not explicitly distinguish these tendencies. Appropriate decisions must also translate into motion that respects vehicle limits. We propose BOLT, a behavior-aware decision-making framework that combines explicit behavioral supervision with kinematically informed trajectory refinement. We construct nuPlan-Behavior, a behavior-annotated extension of nuPlan with motion-derived pseudo-labels. These labels supervise a continuous-time encoder with prototype memory, whose continuous behavioral representations guide surrounding-agent prediction and ego trajectory generation through cross-attention. At inference, differential-flatness-informed refinement adjusts the selected ego trajectory using motion and obstacle-proximity penalties. Evaluations on nuScenes, nuPlan, and CommonRoad demonstrate leading prediction accuracy and closed-loop decision-making performance. The results show that behavioral representations complement scene encoding of motion histories, while trajectory refinement further improves closed-loop performance in challenging scenarios.