DIT4BEARs Smart Roads Internship

TL;DR

This paper describes a project that developed deep learning models to classify road conditions and predict safety metrics for arctic roads, integrating sensor, weather, and map data to improve winter road maintenance safety.

Contribution

The paper presents a novel integrated approach combining deep learning classifiers, safety metrics, and pathfinding algorithms for arctic road safety management.

Findings

Effective classification of road states using deep learning models.

Development of a safety metric based on accident rates and friction.

Implementation of a pathfinding algorithm considering sensor and weather data.

Abstract

The research internship at UiT - The Arctic University of Norway was offered for our team being the winner of the 'Smart Roads - Winter Road Maintenance 2021' Hackathon. The internship commenced on 3 May 2021 and ended on 21 May 2021 with meetings happening twice each week. In spite of having different nationalities and educational backgrounds, we both interns tried to collaborate as a team as much as possible. The most alluring part was working on this project made us realize the critical conditions faced by the arctic people, where it was hard to gain such a unique experience from our residence. We developed and implemented several deep learning models to classify the states (dry, moist, wet, icy, snowy, slushy). Depending upon the best model, the weather forecast app will predict the state taking the Ta, Tsurf, Height, Speed, Water, etc. into consideration. The crucial part was to define a safety metric which is the product of the accident rates based on friction and the accident rates based on states. We developed a regressor that will predict the safety metric depending upon the state obtained from the classifier and the friction obtained from the sensor data. A pathfinding algorithm has been designed using the sensor data, open street map data, weather data.