UAV-based Intelligent Information Systems on Winter Road Safety for Autonomous Vehicles

TL;DR

This paper presents a UAV-based system that segments and estimates winter road width to improve autonomous vehicle safety and decision-making in adverse weather conditions.

Contribution

It introduces a novel method for real-time winter road segmentation and width estimation using UAVs and autonomous vehicle perspectives.

Findings

Accurate road width estimation in winter conditions.

Enhanced autonomous vehicle decision-making capabilities.

Improved safety during adverse weather conditions.

Abstract

As autonomous vehicles continue to revolutionize transportation, addressing challenges posed by adverse weather conditions, particularly during winter, becomes paramount for ensuring safe and efficient operations. One of the most important aspects of a road safety inspection during adverse weather is when a limited lane width can reduce the capacity of the road and raise the risk of serious accidents involving autonomous vehicles. In this research, a method for improving driving challenges on roads in winter conditions, with a model that segments and estimates the width of the road from the perspectives of Uncrewed aerial vehicles and autonomous vehicles. The proposed approach in this article is needed to empower self-driving cars with up-to-date and accurate insights, enhancing their adaptability and decision-making capabilities in winter landscapes.