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Research in autonomous driving for unstructured environments suffers from alack of semantically labeled datasets compared to its urban counterpart. Urbanand unstructured outdoor environments are challenging due to the varyinglighting and weather conditions during a day and across seasons. In this paper,we introduce TAS500, a novel semantic segmentation dataset for autonomousdriving in unstructured environments. TAS500 offers fine-grained vegetation andterrain classes to learn drivable surfaces and natural obstacles in outdoorscenes effectively. We evaluate the performance of modern semantic segmentationmodels with an additional focus on their efficiency. Our experimentsdemonstrate the advantages of fine-grained semantic classes to improve theoverall prediction accuracy, especially along the class boundaries. The datasetand pretrained model are available at mucar3.de/icpr2020-tas500.

A Fine-Grained Dataset and its Efficient Semantic Segmentation for Unstructured Driving Scenarios