# Applying Semantic Segmentation to Autonomous Cars in the Snowy Environment

Source: [Applying Semantic Segmentation to Autonomous Cars in the Snowy Environment](<https://www.sota2.com/research/paper/68fa68061e209131cea198ef>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-27T06:03:45.482Z

Authors: Zhaoyu Pan, Takanori Emaru, Ankit Ravankar, Yukinori Kobayashi

Published: 2020-07-25T07:07:23.000Z

## Abstract

This paper mainly focuses on environment perception in snowy situations which forms the backbone of the autonomous driving technology. For the purpose, semantic segmentation is employed to classify the objects while the vehicle is driven autonomously. We train the Fully Convolutional Networks (FCN) on our own dataset and present the experimental results. Finally, the outcomes are analyzed to give a conclusion. It can be concluded that the database still needs to be optimized and a favorable algorithm should be proposed to get better results.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2007.12869>)

[PDF](<https://arxiv.org/pdf/2007.12869.pdf>)