Accurate simulation is crucial for autonomous driving development, yet capturing real-world traffic complexity remains challenging.
Existing simulators that rely on predefined rules or static data playback struggle with dynamic traffic.
CRITICAL uses real traffic data and a large language model (LLM) to adjust the initial simulation configuration, but the simulated distribution still diverges from real traffic as the rollout evolves.
We propose REARL
REARL is a closed-loop simulation enhancement framework that integrates real traffic data with LLMs.
Real traffic data are clustered, and each cluster center is used as a representative scenario that provides typical real-world traffic patterns for the LLM.
A timed sliding-window detector then monitors discrepancies in vehicle speed distribution and mean spacing between pairs of vehicles.
If a metric exceeds a threshold, the LLM adjusts vehicle decision-making; otherwise the existing controller is kept.
The LLM also selects a matching real vehicle from a traffic snapshot and modulates the simulated vehicle with reference to that real action.
Experiments and Results
In a controlled HighD highway setting, compared with the CRITICAL baseline and a PPO-based learning baseline, REARL reduces the Hellinger distance for speed distributions to 0.3067 and the MAPE for mean spacing to 0.8371, while achieving a time headway (THW) of 22.8575 and a lane change rate of 0.0708.