# CARLA Simulator

Autonomous-driving policies react in closed loop, so a scenario scripted with fixed routes often drifts away from the interaction it was meant to stage, a problem called closed-loop drift. A scenario orchestrator continuously monitors the joint traffic state and adapts non-ego actors through re-timing, re-routing and role reassignment, solving a constrained optimization at each step so the test intent survives while interventions stay limited and traffic stays feasible. In the CARLA simulator across six scenario families and six ego policies, the method fully realizes the intended interaction in every scenario family, while baselines such as ProSim, LASER and OSC2Runner mostly satisfy only the two easiest scenes.

Autonomous-driving policies react in closed loop, so a scenario scripted with fixed routes often drifts away from the interaction it was meant to stage, a problem called closed-loop drift. A scenario orchestrator continuously monitors the joint traffic state and adapts non-ego actors through re-timing, re-routing and role reassignment, solving a constrained optimization at each step so the test intent survives while interventions stay limited and traffic stays feasible. In the CARLA simulator across six scenario families and six ego policies, the method fully realizes the intended interaction in every scenario family, while baselines such as ProSim, LASER and OSC2Runner mostly satisfy only the two easiest scenes.