LitSim: Conflict-aware Policy for Long-term Interactive Traffic Simulation

Simulation is pivotal in evaluating the performance of autonomous driving systems due to the advantages in efficiency and cost compared to on-road testing. Realistic multi-agent behavior~(e.g., interactive and long-term) is needed to narrow the gap between the simulation and the reality. The existing work has the following shortcomings in achieving this goal:~(1) log replay offers realistic scenarios but leads to unrealistic collisions due to lacking dynamic interactions, and~(2) model-based and learning-based solutions encourage interactions but often deviate from real-world data in long horizons. In this work, we propose LitSim, a long-term interactive simulation approach that maximizes realism while avoiding unrealistic collisions. Specifically, we replay the log for most scenarios and intervene only when LitSim predicts unrealistic conflicts. We then encourage interactions among the agents and resolve the conflicts, thereby reducing the likelihood of unrealistic collisions. We train and validate our model on the real-world dataset NGSIM, and the experimental results demonstrate that LitSim outperforms the current popular approaches in realism and reactivity.