Improve traffic safety through advanced and automatic driving evaluations using AI and eye tracking
The aim of the project is to improve traffic safety through advanced and automatic driving evaluations using AI and eye tracking. The project is an extension of an already completed preliminary study of the healthcare system.
Goal
Extension of the feasibility study already conducted with healthcare sector andin progress with Trafikverket.
Effects
Improved driving evaluation method which can change the way the driving evaluations are conducted, thus improving the traffic safety and ultimately contributing to Nollvisionen.
Background
This research direction is a spinoff from the application of eye tracking human intention recogntion in human-robot interaction research with application in industrial logistics.
Summary
In this feasibility study, we investigated various collaboration opportunities and submitted applications for multiple funding proposals through the established connections. The established connections that lead to the submitted proposal are a direct contribution from this feasibility study.
When we have a system like this that automates the process of driving evaluation, it can bring a lot of value to healthcare sites because in healthcare today, for somebody to get a driving evaluation, there needs to be a healthcare professional, always sitting in a vehicle and a traffic school teacher which can have a very long waiting period, For e.g., in Stockholm, it's up to nine months. The idea is that by using our system, we can partially automate the process of driving evaluations and we want to start it by giving a decision support system which we hope will eventually turn into a decision-making system.
Ravi Teja Chadalava, Founder & CEO at QT-PIE
Partners
VTI, QTPIE AB & Örebro University Hospital
