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