  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/
