@article{ref1,
title="Weakly-supervised recommended traversable area segmentation using automatically labeled images for autonomous driving in pedestrian environment with no edges",
journal="Sensors (Basel)",
year="2021",
author="Shino, Motoki and Matsumi, Ryosuke and Onozuka, Yuya",
volume="21",
number="2",
pages="e437-e437",
abstract="Detection of traversable areas is essential to navigation of autonomous personal mobility systems in unknown pedestrian environments. However, traffic rules may  recommend or require driving in specified areas, such as sidewalks, in environments  where roadways and sidewalks coexist. Therefore, it is necessary for such autonomous  mobility systems to estimate the areas that are mechanically traversable and  recommended by traffic rules and to navigate based on this estimation. In this  paper, we propose a method for weakly-supervised recommended traversable area  segmentation in environments with no edges using automatically labeled images based  on paths selected by humans. This approach is based on the idea that a  human-selected driving path more accurately reflects both mechanical traversability  and human understanding of traffic rules and visual information. In addition, we  propose a data augmentation method and a loss weighting method for detecting the  appropriate recommended traversable area from a single human-selected path. Evaluation of the results showed that the proposed learning methods are effective  for recommended traversable area detection and found that weakly-supervised semantic  segmentation using human-selected path information is useful for recommended area  detection in environments with no edges.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s21020437",
url="http://dx.doi.org/10.3390/s21020437"
}