The idea for VHAP builds upon previous work performed at the Department of Electrical Engineering and SAFER Vehicle and Traffic Safety Centre at Chalmers in collaboration with Autoliv and VTI (Swedish National Road and Transport Research Institute) for assessing driver level of sleepiness via Artificial Intelligence (AI), by machine learning of heart rate variability (HRV) signals.
VHAP will enable real-time driver monitoring and capability estimation through non-obtrusive measurement and analysis of driver vital data such as heart rate, HRV and breathing rate, as well as data related to driving performance and context information.
Applications can be found in scenarios expected in autonomous and semi-autonomous vehicles, as well as the possibility to detect health issues that can influence the driving capability, or in post-crash situations. The research is expected to contribute to Euro NCAP’s future rating of driver monitoring, which will include detection of driver fatigue, distraction and driving under the influence of alcohol.
Contributions can include best practice, guidance on suitable state indicators and simplified test procedures, and information on expected accuracy with industry-grade sensors.