GarchingSim: An Autonomous Driving Simulator with Photorealistic Scenes and Minimalist Workflow
2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC)(2024)
摘要
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.
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关键词
Autonomous Vehicles,Photo-realistic Scenes,Vehicle Dynamics,Vehicle Dynamics Model,Open-source Simulation,Convolutional Neural Network,Simulation Environment,Bounding Box,Real-world Scenarios,Path Planning,Inertial Measurement Unit,Intelligence Agencies,Traffic Light,Global Navigation Satellite System,3D Environment,Internal Combustion Engine,Advances In Deep Learning,Sensor Readings,Traffic Scenarios,Advanced Driver Assistance Systems,Imitation Learning,Robot Operating System,Game Engine,Real-world Test,Unreal Engine,Semantic Segmentation,External Software,RGB Camera,Pedestrian,Traffic Flow
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