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

• URL: http://arxiv.org/abs/2502.01894v1
• Date: Tue, 04 Feb 2025 00:00:06 GMT
• Title: SimBEV: A Synthetic Multi-Task Multi-Sensor Driving Data Generation Tool and Dataset
• Authors: Goodarz Mehr, Azim Eskandarian,
• Abstract summary: Bird's-eye view (BEV) perception for autonomous driving has garnered significant attention in recent years. We introduce SimBEV, a synthetic data generation tool that incorporates information from multiple sources to capture accurate BEV ground truth data. We use SimBEV to create the SimBEV dataset, a large collection of annotated perception data from diverse driving scenarios.
• Score: 101.51012770913627
• License:
• Abstract: Bird's-eye view (BEV) perception for autonomous driving has garnered significant attention in recent years, in part because BEV representation facilitates the fusion of multi-sensor data. This enables a variety of perception tasks including BEV segmentation, a concise view of the environment that can be used to plan a vehicle's trajectory. However, this representation is not fully supported by existing datasets, and creation of new datasets can be a time-consuming endeavor. To address this problem, in this paper we introduce SimBEV, an extensively configurable and scalable randomized synthetic data generation tool that incorporates information from multiple sources to capture accurate BEV ground truth data, supports a comprehensive array of sensors, and enables a variety of perception tasks including BEV segmentation and 3D object detection. We use SimBEV to create the SimBEV dataset, a large collection of annotated perception data from diverse driving scenarios.

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