  Decision making in automated driving is highly specific to the environment
and thus semantic segmentation plays a key role in recognizing the objects in
the environment around the car. Pixel level classification once considered a
challenging task which is now becoming mature to be productized in a car.
However, semantic annotation is time consuming and quite expensive. Synthetic
datasets with domain adaptation techniques have been used to alleviate the lack
of large annotated datasets. In this work, we explore an alternate approach of
leveraging the annotations of other tasks to improve semantic segmentation.
Recently, multi-task learning became a popular paradigm in automated driving
which demonstrates joint learning of multiple tasks improves overall
performance of each tasks. Motivated by this, we use auxiliary tasks like depth
estimation to improve the performance of semantic segmentation task. We propose
adaptive task loss weighting techniques to address scale issues in multi-task
loss functions which become more crucial in auxiliary tasks. We experimented on
automotive datasets including SYNTHIA and KITTI and obtained 3% and 5%
improvement in accuracy respectively.
