The research group CPSForsk was central in the Østfold University College focus area Technology-Energy-Society, and we have been partner in the large ECSEL lighthouse project Productive4.0. The results of the Norwegian consortium is featured in an article on the home page of the project titles "Prediction, online analyzing, sensors - Norwegians taking control". If you want to see a 6 minutes movie about what we are doing at Unger Fabrikker watch this movie:
Our most recent project is also an ECSEL project: Arrowhead Tools building on the results from Productive4.0. This project also has a large number of European partners (about 90). Arrowhead Tools lasted May 2019 - July 2022. Our PhD fellow at HIOF in this project will finalize her work in the next year. Professor Ø. Haugen was work package leader for Requirements management in the project and also task leader for standardization of languages.
CriSp (CRItical SPeed function and Automatic speed control function ahead of a dangerous road section) is a project that falls within the area of Cooperative and Intelligent transportation systems (C-ITS), and it aims to : (1) develop a safety solution that proactively monitors, warns, and autonomously controls the speed of the vehicle at curved or hilly road sections, and, (2) to make this solution work effectively on roads with different levels of construction and wear, for vehicles with varying levels of tyre wear, and as road conditions vary with the weather. The novelty in this C-ITS architecture consists of the following: a module made up of on-board vehicle electronics, a computing module in a Road side unit (RSU), and short-range wireless communication between the vehicles and the RSU. By using short range communication standards such as WiFi and ITS-G5, and by using cellular communication only when available, we allow this solution to be deployed even at remote locations that have no reliable cellular connectivity. By not relying exclusively on manufacturer’s clouds, we allow the RSU to calculate its critical speed based on data from vehicles of different manufacturers.
We will estimate the critical speed by collecting friction and skidding and slipping related sensor data from as many of the vehicles that recently passed through the given curved section as possible. And by processing this data in real-time, we obtain a real-time version of the critical speed.