SimBEV: A Synthetic Multi-Task Multi-Sensor Driving Data Generation Tool and Dataset
This addresses a data bottleneck for researchers and developers in autonomous driving by offering an open, multi-sensor dataset, though it is incremental as it builds on existing synthetic data generation approaches.
The authors tackled the lack of datasets supporting bird's-eye view (BEV) perception in autonomous driving by introducing SimBEV, a synthetic data generation tool and dataset, which provides extensive, configurable, and scalable annotated data for tasks like BEV segmentation and 3D object detection.
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.