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仿真器的核心模块。然而,大多数可用的仿真器不能够精确地复制真实世界数据的交通模式,并模拟类似于人类的反应性响应,以测试自动驾驶策略。针对这个问题,我们提出了Realistic Interactive TrAffic flow(RITA),它作为现有驾驶仿真器的集成组件,为测试驾驶策略的评估和优化提供高质量的交通流。RITA具有三个关键特征考虑,即保真度、多样性和可控性,由两个核心模块RITABackend和RITAKit组成。RITABackend支持基于车辆的控制,并提供来自真实世界数据集的交通流生成模型,而RITAKit则开发了易于使用的接口,通过RITABackend实现可控的交通流生成。我们展示了RITA在几种高度交互的高速公路场景中创建不同、高保真度的交通流的能力。实验结果表明,我们生成的RITA交通流具有三个关键特征,可以增强驾驶策略评估的完整性。此外,我们展示了通过在线微调RITA交通流来进一步改进基线策略的可能性。