Enhancing SUMO simulator for simulation based testing and validation of autonomous vehicles

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 traffic simulation platform, has found use as an AV simulator but suffers from similar issues which makes 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 randomly from the parameter distributions to create realistic background vehicle driving behavior. Secondly, we combine SUMO with OpenAI gym, creating a Python package placed in a docker container 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. For the calibration, we provide results using simulated and real-life data. For the Sumo-Gym package, we showcase a simple AV platform which runs IDM and lane change throughout the highway loop and provide some qualitative results. Our aim through these enhancements is to provide an easy-to-use simulation environment which can be installed in any operating platform and can be readily used for AV testing and validation.