High-quality traffic flow generation is the core module in building simulators for autonomous driving. However, the majority of available simulators are incapable of replicating traffic patterns that accurately reflect the various features of real-world data while also simulating human-like reactive responses to the tested autopilot driving strategies. Taking one step forward to addressing such a problem, we propose Realistic Interactive TrAffic flow (RITA) as an integrated component of existing driving simulators to provide high-quality traffic flow for the evaluation and optimization of the tested driving strategies. RITA is developed with consideration of three key features, i.e., fidelity, diversity, and controllability, and consists of two core modules called RITABackend and RITAKit. RITABackend is built to support vehicle-wise control and provide traffic generation models from real-world datasets, while RITAKit is developed with easy-to-use interfaces for controllable traffic generation via RITABackend. We demonstrate RITA's capacity to create diversified and high-fidelity traffic simulations in several highly interactive highway scenarios. The experimental findings demonstrate that our produced RITA traffic flows exhibit all three key features, hence enhancing the completeness of driving strategy evaluation. Moreover, we showcase the possibility for further improvement of baseline strategies through online fine-tuning with RITA traffic flows.

翻译：高质量交通流生成是构建自动驾驶模拟器的核心模块。然而，现有大多数模拟器既无法复现准确反映真实世界数据多样特征的交通模式，也无法模拟被测自动驾驶策略所引发的类人反应行为。为向解决该问题迈进一步，我们提出真实交互交通流（RITA）作为现有驾驶模拟器的集成组件，为被测驾驶策略的评估与优化提供高质量交通流。RITA的开发兼顾三大关键特性——保真度、多样性与可控性，并由RITABackend与RITAKit两个核心模块构成。RITABackend支持车辆级控制，并提供基于真实世界数据集的交通生成模型；RITAKit则通过易用接口实现基于RITABackend的可控交通生成。我们在多个高交互性高速公路场景中验证了RITA生成多样化、高保真交通仿真的能力。实验结果表明，生成的RITA交通流同时展现上述三大关键特性，从而提升了驾驶策略评估的完整性。此外，我们展示了通过RITA交通流在线微调可进一步改进基线策略的可行性。