TADAP: Trajectory-Aided Drivable area Auto-labeling with Pre-trained
  self-supervised features in winter driving conditions
Eerik Alamikkotervo Risto Ojala Alvari Seppänen Kari Tammi
Detection of the drivable area in all conditions is crucial for autonomous
driving and advanced driver assistance systems. However, the amount of labeled
data in adverse driving conditions is limited, especially in winter, and
supervised methods generalize poorly to conditions outside the training
distribution. For easy adaption to all conditions, the need for human
annotation should be removed from the learning process. In this paper,
Trajectory-Aided Drivable area Auto-labeling with Pre-trained self-supervised
features (TADAP) is presented for automated annotation of the drivable area in
winter driving conditions. A sample of the drivable area is extracted based on
the trajectory estimate from the global navigation satellite system. Similarity
with the sample area is determined based on pre-trained self-supervised visual
features. Image areas similar to the sample area are considered to be drivable.
These TADAP labels were evaluated with a novel winter-driving dataset,
collected in varying driving scenes. A prediction model trained with the TADAP
labels achieved a +9.6 improvement in intersection over union compared to the
previous state-of-the-art of self-supervised drivable area detection.