  Simulation is pivotal in evaluating the performance of autonomous driving
systems due to the advantages of high efficiency and low cost compared to
on-road testing. Bridging the gap between simulation and the real world
requires realistic agent behaviors. However, the existing works have the
following shortcomings in achieving this goal: (1) log replay offers realistic
scenarios but often leads to collisions due to the absence of dynamic
interactions, and (2) both heuristic-based and data-based solutions, which are
parameterized and trained on real-world datasets, encourage interactions but
often deviate from real-world data over long horizons. In this work, we propose
LitSim, a long-term interactive simulation approach that maximizes realism by
minimizing the interventions in the log. Specifically, our approach primarily
uses log replay to ensure realism and intervenes only when necessary to prevent
potential conflicts. We then encourage interactions among the agents and
resolve the conflicts, thereby reducing the risk of unrealistic behaviors. We
train and validate our model on the real-world dataset NGSIM, and the
experimental results demonstrate that LitSim outperforms the currently popular
approaches in terms of realism and reactivity.
