Bridging Local Observation and Global Simulation in Closed-Loop Traffic Modeling

Autoregressive traffic simulators learn from local, ego-centric driving logs but must generate globally coherent multi-agent interactions. Limited observation range leaves the context incomplete for non-ego agents, particularly those far from the ego vehicle. Simulators may therefore learn actions without observing the conditions that induced them and reproduce those actions in contexts where they are inappropriate—a problem we term the ***local-to-global context mismatch***. We introduce ***CRAFT***, **C**ontextual p**R**eference **A**lignment **F**ramework for **T**raffic Simulation, a lightweight, plug-and-play framework that uses closed-loop rollouts to expose and rectify such behaviors. Agnostic to the base simulator’s architecture, motion prior, and training strategy, CRAFT operates post hoc without retraining or modifying the simulator. CRAFT labels grouped rollouts using traffic-rule and driving-quality criteria, then trains a separate Contextual Preference Evaluator (CPE) through contrastive preference learning over normal and failure cases within trajectories and across rollouts. At inference, the CPE uses lookahead to evaluate candidate scene evolutions and reweights action probabilities toward contextually appropriate behavior. Across three state-of-the-art simulators on the Waymo Open Motion Dataset, CRAFT reduces scene-level collision, offroad, and traffic-violation rates by up to 31.2%, 14.9%, and 33.2%, respectively, while improving motion realism. These results ***establish contextual preference alignment*** as a general and efficient mechanism for mitigating local-to-global mismatch ***across heterogeneous traffic simulators***.