Conducting real road testing for autonomous driving algorithms can be expensive and sometimes impractical, particularly for small startups and research institutes. Thus, simulation becomes an important method for evaluating these algorithms. However, the availability of free and open-source simulators is limited, and the installation and configuration process can be daunting for beginners and interdisciplinary researchers. We introduce an autonomous driving simulator with photorealistic scenes, meanwhile keeping a user-friendly workflow. The simulator is able to communicate with external algorithms through ROS2 or Socket.IO, making it compatible with existing software stacks. Furthermore, we implement a highly accurate vehicle dynamics model within the simulator to enhance the realism of the vehicle's physical effects. The simulator is able to serve various functions, including generating synthetic data and driving with machine learning-based algorithms. Moreover, we prioritize simplicity in the deployment process, ensuring that beginners find it approachable and user-friendly.
@article{arxiv.2401.15803,
title = {GarchingSim: An Autonomous Driving Simulator with Photorealistic Scenes and Minimalist Workflow},
author = {Liguo Zhou and Yinglei Song and Yichao Gao and Zhou Yu and Michael Sodamin and Hongshen Liu and Liang Ma and Lian Liu and Hao Liu and Yang Liu and Haichuan Li and Guang Chen and Alois Knoll},
journal= {arXiv preprint arXiv:2401.15803},
year = {2024}
}