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.

翻译：仿真因具有高效、低成本的优势，相较于道路测试，在评估自动驾驶系统性能中发挥着关键作用。弥合仿真与真实世界之间的差距，需要模拟逼真的智能体行为。然而，现有工作在实现这一目标时存在以下不足：（1）日志回放虽能提供真实场景，但因缺乏动态交互常导致碰撞；（2）基于启发式和数据驱动的解决方案（在真实数据集上参数化并训练）虽能促进交互，但长期运行时往往偏离真实数据分布。本文提出LitSim，一种通过最小化对日志的干预来最大限度提升真实性的长期交互式仿真方法。具体而言，该方法主要采用日志回放确保真实性，仅在必要时介入以防止潜在冲突。随后，我们促使智能体之间开展交互并解决冲突，从而降低非真实行为出现的风险。我们在真实数据集NGSIM上训练并验证模型，实验结果表明，LitSim在真实性与反应性方面均优于目前主流方法。