Current autonomous vehicle (AV) simulators are built to provide large-scale testing required to prove capabilities under varied conditions in controlled, repeatable fashion. However, they have certain failings including the need for user expertise and complex inconvenient tutorials for customized scenario creation. Simulation of Urban Mobility (SUMO) simulator, which has been presented as an open-source AV simulator, is used extensively but suffer from similar issues which make it difficult for entry-level practitioners to utilize the simulator without significant time investment. In that regard, we provide two enhancements to SUMO simulator geared towards massively improving user experience and providing real-life like variability for surrounding traffic. Firstly, we calibrate a car-following model, Intelligent Driver Model (IDM), for highway and urban naturalistic driving data and sample automatically from the parameter distributions to create the background vehicles. Secondly, we combine SUMO with OpenAI gym, creating a Python package which can run simulations based on real world highway and urban layouts with generic output observations and input actions that can be processed via any AV pipeline. Our aim through these enhancements is to provide an easy-to-use platform which can be readily used for AV testing and validation.
翻译:现有自主车辆模拟器(AV)的建造是为了提供大规模测试,以便在各种条件下,以可控制、可重复的方式,在可控制、可重复的方式,证明能力所需的大规模测试,然而,这些模拟器有某些缺陷,包括需要用户专门知识和复杂的不便辅导,以建立定制的情景。城市流动模拟器(SUMO)模拟器作为开放源的AV模拟器被广泛使用,但也有类似的问题,这使得初级操作员难以在不投入大量时间的情况下使用模拟器。在这方面,我们为SUMO模拟器提供了两个增强装置,该装置旨在大规模改善用户经验,并提供真实生活,如周围交通的变异性。首先,我们校准了一个汽车跟踪模型,即智能驱动器模型(IDM),用于从参数分布中自动获得的高速公路和城市自然驱动数据和样本,以创建背景车辆。第二,我们将SUMO与OpenAI健身房结合起来,创建一套Python软件包,可以在真实世界高速公路和城市布局上进行模拟,并配有通用的产出观测和输入动作,可以通过任何AV管道进行快速的测试。我们的目标通过这些测试可以轻易地加以验证。