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

arXiv: https://arxiv.org/abs/2406.12370 · HTML: https://mlsanity.com/paper/2406.12370/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 17 references that are in the map.
- First posted on arXiv 18 Jun 2024.

## Builds on (most cited first, 2 of 2)

- [KISS-ICP: In Defense of Point-to-Point ICP -- Simple, Accurate, and Robust Registration If Done the Right Way](https://mlsanity.com/paper/2209.15397/) (2022)
- [SHINE-Mapping: Large-Scale 3D Mapping Using Sparse Hierarchical Implicit Neural Representations](https://mlsanity.com/paper/2210.02299/) (2022)

## Abstract (arXiv)

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.