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

TL;DR

This paper enhances the SUMO simulator by calibrating a car-following model and integrating it with OpenAI gym, making it more user-friendly and realistic for autonomous vehicle testing and validation.

Contribution

The authors introduce two key improvements to SUMO: automatic calibration of the IDM model for realistic traffic and integration with OpenAI gym for easier simulation setup.

Findings

Improved realism in traffic simulation through calibrated IDM parameters.

Enhanced user experience with a Python interface for AV testing.

Facilitated entry-level access to AV simulation environments.

Abstract

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.