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.
翻译:汽车上安装的多个传感器对现场的准确理解是自主驾驶系统的关键要求。 如今,这项任务主要通过数据饥饿的深层学习技术来完成,这些技术需要培训大量数据。由于进行分解标签的成本很高,因此提出了许多合成数据集。然而,其中多数数据错过了数据的多传感器性质,没有捕捉因日间和天气条件变化而带来的重大变化。为了填补这些空白,我们引入了SELMA,这是用于语义分解的新合成数据集,包含从24个不同传感器(包括RGB、深度、语义照相机和LIDARs)获得的30K以上独特路径,这些传感器需要大量培训。在27个不同的大气和日间条件下,共20多个样本。SELMA以CARA为基础,一个在自主驾驶情景中生成合成数据的开源模拟器,我们为增加场景和班组的变异性和多样性,并与其他基准数据集相协调。正如实验性评估所显示的那样,SELMA允许在27个不同的大气和日间条件下进行高效的公开科学研究,从而实现了可贵度和多式的标准和公开学习。