Leveraging Road Area Semantic Segmentation with Auxiliary Steering Task
This work addresses the problem of data annotation inefficiency for autonomous driving systems, but it is incremental as it builds on existing CNN-based methods with a small performance improvement.
The paper tackles the challenge of improving road area semantic segmentation in autonomous driving by leveraging steering wheel angle data to reduce the need for manual annotation, resulting in a 0.1-2.9% gain in mIoU compared to a reference model.
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.