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.
翻译:挪威北极大学UiT的研究实习是为我们的团队赢得“Smart Roads-Winter Road 2021年冬季道路维护”Hackathon而提供的。实习于2021年5月3日开始,于2021年5月21日结束,每周开会两次。尽管我们具有不同的国籍和教育背景,但我们都试图尽可能地作为一个团队协作。这个项目最有意义的部分是,我们认识到北极人所面临的关键条件,在那里很难从我们的住所获得如此独特的经验。我们开发并实施了数个深层次的学习模型,对各州进行分类(干燥、湿、湿、冰雪、雪、冲浪)。根据最佳模型,天气预报应用程序将预测采用塔苏尔夫、海ight、速度、水等,并尽可能考虑。关键部分是确定一个安全指标,这是事故率的产物,这是根据摩擦和基于各州的事故率得出的。我们开发了一个回收者,它将根据从分析者那里获得的状态预测安全度指标,而从天气和摩擦度上从感官数据路径上设计的数据分析数据。一个分析算出。一个关键部分是利用从分析器数据路路路段。