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

TL;DR

SimBEV is a versatile synthetic data generation tool that creates large, annotated multi-sensor datasets for autonomous driving perception tasks, addressing the scarcity of BEV datasets.

Contribution

The paper introduces SimBEV, a configurable and scalable synthetic data generator supporting multiple sensors and perception tasks, with an accompanying open dataset.

Findings

Supports diverse sensors and perception tasks

Generates accurate BEV ground truth data

Enables large-scale dataset creation

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.