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 fidelity, diversity, and controllability in consideration, 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 meet all three design goals, 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：高质量的交通流量生成是建立自动驾驶模拟器的核心模块,然而,大多数现有模拟器无法复制准确反映真实世界数据不同特点的交通模式,同时模拟对测试过的自动驾驶战略的像人一样的反应性反应。向前一步,我们提议现实互动交通流量(RITA)作为现有驾驶模拟器的一个综合组成部分,为评估和优化测试过的驾驶战略提供高质量的交通流量。RITA是本着忠诚、多样性和可控性考虑而开发的,由两个核心模块组成,即RITABackend和RITAKit。RITABackend的构建是为了支持车辆的明智控制并提供来自真实世界数据集的交通生成模型,而RITAKit(RIT)则以易于使用的界面开发,用于通过RITABackend进行控制性交通生成。我们展示了RITA的能力,在考虑中的多样化和高纤维性交通模拟中,在几个高度互动的高速公路情景下,由两个核心模块构成。RITA的实验性动态展示了我们通过在线交通流量制定的所有趋势。