  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.
