Driver assistance systems and electronics (e.g. navigators, cell phones, etc.) steal increasing amounts of driver attention. Therefore, the vehicle industry is striving to build a driving environment where input–output devices are smartly scheduled, allowing sufficient time for the driver to focus attention on the surrounding traffic. To enable a smart human–machine interface (HMI), the driver’s momentary state needs to be measured. This paper describes a facility for monitoring the distraction of a driver and presents some early evaluation results. The module is able to detect the driver’s visual and cognitive workload by fusing stereo vision and lane tracking data, running both rule–based and support-vector machine (SVM) classification methods. The module has been tested with data from a truck and a passenger car. The results show over 80% success in detecting visual distraction and a 68–86 % success in detecting cognitive distraction, which are satisfactory results.
|Journal||Proceedings of the Institution of Mechanical Engineers Part D: Journal of Automobile Engineering|
|Publication status||Published - 2007|
|MoE publication type||A1 Journal article-refereed|
- Cognitive distraction
- machine vision
- driver monitoring
Kutila, M., Jokela, M., Mäkinen, T., Viitanen, J., Markkula, G., & Victor, T. (2007). Driver cognitive distraction detection: Feature estimation and implementation. Proceedings of the Institution of Mechanical Engineers Part D: Journal of Automobile Engineering, 221(9), 1027-1040. https://doi.org/10.1243/09544070JAUTO332