Automated Driving
Modern vehicles are becoming smarter entities able to understand their environment and autonomously adapt to it. Currently most of the vehicle sensors are monitoring the surrounding conditions while there is a bare minimum understanding of the driver state. The combination of wearable and ubiquitous computing can play a major role in creating a better functioning human-automation system. Such a system will be able to understand and dynamically adapt to the driver’s needs and psychophysiological status, and hence become safer and more reliable. This project addresses the human-computer interaction in the vehicle of tomorrow taking into account the user status and consequently adapting the interface to the user (performance, displays, interaction modalities, etc.) in order to increase safety and reliability.
In this context, a psychophysiological model of the driver needs to be developed. Such a model will allow investigating the tailoring of the interface with the vehicle. The impact of this model is linked to the feasibility of capturing sleepiness, driver fatigue and driver’s attention in a continuous and real-time way. A special emphasis is set on the adequate and continuous detection of situation awareness while driving. In this regard, intelligent multimodal fusion of brain and peripheral signals will be investigated, with the goal of increasing the accurate detection of the driver’s fitness to drive.
Bachelor and Master Theses conducted on this topic
- Minot Océane, Rege Colet Laura, Riss Stanislav, Schöpfer Christian (2019). Psychopyhsiological indicators for situation awareness in semi-autonomous driving.
Photo credit: unifr.ch/psycho