  Accurate scene understanding from multiple sensors mounted on cars is a key
requirement for autonomous driving systems. Nowadays, this task is mainly
performed through data-hungry deep learning techniques that need very large
amounts of data to be trained. Due to the high cost of performing segmentation
labeling, many synthetic datasets have been proposed. However, most of them
miss the multi-sensor nature of the data, and do not capture the significant
changes introduced by the variation of daytime and weather conditions. To fill
these gaps, we introduce SELMA, a novel synthetic dataset for semantic
segmentation that contains more than 30K unique waypoints acquired from 24
different sensors including RGB, depth, semantic cameras and LiDARs, in 27
different atmospheric and daytime conditions, for a total of more than 20M
samples. SELMA is based on CARLA, an open-source simulator for generating
synthetic data in autonomous driving scenarios, that we modified to increase
the variability and the diversity in the scenes and class sets, and to align it
with other benchmark datasets. As shown by the experimental evaluation, SELMA
allows the efficient training of standard and multi-modal deep learning
architectures, and achieves remarkable results on real-world data. SELMA is
free and publicly available, thus supporting open science and research.
