We present a novel synthetically generated multi-modal dataset, SCaRL, to enable the training and validation of autonomous driving solutions. Multi-modal datasets are essential to attain the robustness and high accuracy required by autonomous systems in applications such as autonomous driving. As deep learning-based solutions are becoming more prevalent for object detection, classification, and tracking tasks, there is great demand for datasets combining camera, lidar, and radar sensors. Existing real/synthetic datasets for autonomous driving lack synchronized data collection from a complete sensor suite. SCaRL provides synchronized Synthetic data from RGB, semantic/instance, and depth Cameras; Range-Doppler-Azimuth/Elevation maps and raw data from Radar; and 3D point clouds/2D maps of semantic, depth and Doppler data from coherent Lidar. SCaRL is a large dataset based on the CARLA Simulator, which provides data for diverse, dynamic scenarios and traffic conditions. SCaRL is the first dataset to include synthetic synchronized data from coherent Lidar and MIMO radar sensors. The dataset can be accessed here: https://fhr-ihs-sva.pages.fraunhofer.de/asp/scarl/

翻译：本文提出了一种新颖的合成生成多模态数据集SCaRL，旨在支持自动驾驶解决方案的训练与验证。多模态数据集对于实现自动驾驶等应用中自主系统所需的鲁棒性与高精度至关重要。随着基于深度学习的解决方案在目标检测、分类与跟踪任务中日益普及，对融合摄像头、激光雷达与雷达传感器的数据集需求巨大。现有自动驾驶真实/合成数据集缺乏完整传感器套件的同步数据采集。SCaRL提供来自RGB相机、语义/实例分割相机及深度相机的同步合成数据；来自雷达的距离-多普勒-方位角/俯仰角地图与原始数据；以及来自相干激光雷达的语义、深度与多普勒数据三维点云/二维地图。SCaRL是基于CARLA模拟器构建的大规模数据集，可为多样化动态场景与交通条件提供数据。SCaRL是首个包含相干激光雷达与MIMO雷达传感器同步合成数据的数据集。数据集访问地址：https://fhr-ihs-sva.pages.fraunhofer.de/asp/scarl/