  Robustness of different pattern recognition methods is one of the key
challenges in autonomous driving, especially when driving in the high variety
of road environments and weather conditions, such as gravel roads and snowfall.
Although one can collect data from these adverse conditions using cars equipped
with sensors, it is quite tedious to annotate the data for training. In this
work, we address this limitation and propose a CNN-based method that can
leverage the steering wheel angle information to improve the road area semantic
segmentation. As the steering wheel angle data can be easily acquired with the
associated images, one could improve the accuracy of road area semantic
segmentation by collecting data in new road environments without manual data
annotation. We demonstrate the effectiveness of the proposed approach on two
challenging data sets for autonomous driving and show that when the steering
task is used in our segmentation model training, it leads to a 0.1-2.9% gain in
the road area mIoU (mean Intersection over Union) compared to the corresponding
reference transfer learning model.
