News

SAFER partners are set to embark on a new Vinnova funded project, "Support Cognitive Abilities for Predictive Processing through Driver Monitoring." Led by the University of Skövde, the initiative aims to improve Advanced Driver Assistance Systems (ADAS) further by focusing on cognitive predictive interaction and driver monitoring for heightened situational awareness.

The primary objective of the project is to analyse and evaluate cognitive factors influencing the interaction between different traffic situations, drivers of varying demographics, and information from driver monitoring systems and ADAS. By conducting driving experimental simulations, the research team will identify cognitive predictive limitations, seeking to enhance reliable interaction with ADAS and ultimately improve traffic safety.

Individually adaptable systems
The project's significance lies in its potential to create more individually adaptable driver support systems. Through in-depth analysis of variables such as traffic situations, driver characteristics, reaction time, and accuracy in crash avoidance, the researchers aim to identify key cognitive prediction factors. This information will inform the development of advanced driver monitoring and support functions tailored to address these factors, fostering a higher level of traffic safety.

This innovative project holds the promise of not only advancing ADAS technology but also significantly contributing to the ongoing efforts to enhance road safety through cutting-edge research and development.

Interdisciplinary expertise from University of Skövde and Smart Eye
Associate Professor Paul Hemeren, the project coordinator and cognitive scientist, along with Senior Lecturer and Researcher Joe Steinhauer, specialist in AI and information fusion, will lead the research efforts. Their expertise in computation, cognition, interaction, and experimentation ensures a holistic approach to traffic safety research. Collaborating with Smart Eye, the project will leverage their knowhow in collecting potential interactive driver behavior data, providing a foundation for cognitive predictive interaction with driver monitoring.

The timeline of the project spans from November 2023, to end of October, 2024.