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arXiv · 2502.01894

SimBEV: A Synthetic Multi-Task Multi-Sensor Driving Data Generation Tool and Dataset

Goodarz Mehr·Azim Eskandarian
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Abstract

Bird's-eye view (BEV) perception has garnered significant attention in autonomous driving in recent years, in part because BEV representation facilitates multi-modal sensor fusion. BEV representation enables a variety of perception tasks including BEV segmentation, a concise view of the environment useful for planning a vehicle's trajectory. However, this representation is not fully supported by existing datasets, and creation of new datasets for this purpose can be a time-consuming endeavor. To address this challenge, we introduce SimBEV. SimBEV is a randomized synthetic data generation tool that is extensively configurable and scalable, supports a wide array of sensors, incorporates information from multiple sources to capture accurate BEV ground truth, and enables a variety of perception tasks including BEV segmentation and 3D object detection. SimBEV is used to create the SimBEV dataset, a large collection of annotated perception data from diverse driving scenarios. SimBEV and the SimBEV dataset are open and available to the public.

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Goodarz Mehr, Azim Eskandarian. 2025-02-04. SimBEV: A Synthetic Multi-Task Multi-Sensor Driving Data Generation Tool and Dataset. https://arxiv.org/abs/2502.01894

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