Finding a CRItical SPeed function ahead of a road section for vehicles in motion

The project will develop a proactive system that prevents a dangerous traffic situation from occurring. Ie. a technology that warns of slippery and or dangerous conditions ahead. In addition, it would adjust the speeds, by giving the vehicle a clear quantitative command in the form of a situation-adapted "speed limit". Such a system may be based on data collected and transmitted by vehicles that have previously driven on the same stretch of road. The project started the second quarter of 2020, and with a new project manager from 1 December 2020. An essential part of the project's progress is the PhD position at the University of Oslo (partner) in WP 2 Data analytics. Due to the Covid-19 pandemic, visas and entry into Norway were postponed, and it was impossible for the person to travel to Norway. In the early autumn of 2021, a new PhD position has been advertised at UiO, and the PhD student will hopefully start by 1 May 2022. The project has implemented what has been possible within this restriction. Among other things, delivery D 1.1 from WP1 Sensing has been completed (Motion assessment study, Deliverable D 1.1 from WP 1 Sensing), and a scientific article has been published (WP2 Wireless communication). The PhD student that is working at Østfold University College has just recently completed the third semester review, and is making good progress. In specific, the following two tasks are close to completion: (i) setting up a simulator for recreating the traffic scenarios, and (ii) setting up an indoor, scaled down track for conducting skidding experiments with remote controlled toy cars. The project has also held meetings with stakeholders such as Volvo and the Norwegian Public Roads Administration, and held its own meeting with the Norwegian Public Roads Administration. Both parts were completed during the first quarter of 2021.

Our overall approach is based on Leveson's STAMP methodology for safety system design, and hence our solution continuously monitors and responds to safety margins. The CriSp solution shall be implemented on a road section by: (i) installing a Road side unit (RSU) with computing resources for calculating the critical speed, (ii) reconfiguring and augmenting vehicular electronics for sensing, motion assessment and cruise control, and (iii) enabling wireless communications between vehicles and the RSU. We structure the Critical speed function with the following actions: (A1) The RSU detects approaching vehicles and sends them its current speed advisory, (A2) if the Autonomous speed control function is enabled on a vehicle, then this function regulates the speed, to obey the RSU's advisory, (A3) every vehicle transmits to the RSU a Motion assessment summary (MAS) as it leaves the section; the MAS records the speed, as well as signals that convey the extent of skidding and rollover, or the margins towards skidding and rollover, and (A4) the RSU updates its critical speed by feeding the received sequence of MASes into a data analytics engine. To implement the V2I and I2V communications of actions A1, A4 we adopt existing standards for short range communications (ITS-G5, WiFi), and Cellular communications if available to the RSU. To implement the sensing, motion assessment and cruise control parts of actions A2, A3 we use existing inertial sensors and ACC technology on board vehicles, and a promising new sensor for measuring tyre-road friction. To develop the CriSp solution we need to develop and test the whole solution via its parts: Sensing, Wireless communication, Data analytics, and Control components. Both the project's internal structure and the team structure match these components. Moreover, the project team brings expertise and research resources in Sensing technology, Vehicle dynamics, Vechicle testing, Transport safety science, ICT and Mechatronics.