REARL: REFERENCE-GUIDED ONLINE CALIBRATION OF TRAFFIC SIMULATION FOR AUTONOMOUS DRIVING

Accurate traffic simulation is important for developing and evaluating autonomous driving systems, but local driving rules and fixed initial conditions can drift from real traffic as a rollout evolves. Existing rule-based, data-driven, and initialization- focused methods either provide limited adaptation during a rollout or replace the native simulator with a learned policy. We propose REARL, a reference-guided online calibration framework that corrects this drift while retaining the simulator’s native controller. REARL builds a library of representative scenes from the highD highway trajectory dataset, retrieves a reference from the observed simulation prefix, and measures speed-distribution and spacing discrepancies at scheduled checkpoints. When a discrepancy exceeds a calibrated threshold, a large language model (LLM) selects one simulator-native action from a time-aligned real-traffic snapshot; otherwise, the Intelligent Driver Model (IDM) with the Minimizing Overall Braking Induced by Lane changes (MOBIL) controller (IDM+MOBIL) continues unchanged. In a paired development study on highD scenes, REARL– Qwen reduces speed Hellinger distance from 0.3313 to 0.2945 and spacing error from 0.9190 to 0.8448 relative to Base. Component controls show that updating the reference improves speed alignment, deterministic correction achieves lower spacing error at a substantial jerk and headway cost, and a retrospective matched- budget periodic comparator has lower error than sparse discrepancy triggering. These results support online reference-guided calibration as a practical middle ground between fixed initialization and full policy replacement.