Improvements in Driver-Vehicle Interaction: Selected Cases
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Technological advances, miniaturization, and accessible wireless broadband communication have fertilized pervasive and ubiquitous computing. First pervasive/ubicomp architectures emerged as rather simple sensor/actuator couplings concerned with basic issues such as activity or context recognition, later broadened to include resource-, situation-, ?, and self-awareness. Recent pervasive ICT research is facing a new challenge. It has to tackle problems related to cognitive capacities, behavioral patterns, and the social context of individuals. This is particularly evidenced by the trends in the automotive embedded systems, where both the individual driver and (spontaneous formed) car collectives could gain from new technologies. To address this gap, I will present our driver-vehicle co-model (DVC-model), expressing the complex interactions between the human driver and the in-car and on-car technologies, and aimed at understanding adaptation phenomena rooted in the feedback loops among individual drivers and their cars. I will further demonstrate improvements in driver-vehicle interaction with selected cases (e.g., recognition, perception, implicit interaction, subliminal stimulation, human sense extension, attention management).
Adaptation in this driver-vehicle feedback loop is directly translating into an individuals driving style, and perceiving the driving style of other drivers, in turn, influences the emotional state and hence driving style of all the observers. These transitional, yet collective driver state and driving style changes raise global traffic phenomena like jams, collective aggressiveness, etc. To address this problem, the DVC-model is extended in the sense that sensor readings recognizing the driver state are propagated into a collective driver-vehicle co-model (CDVC), which in turn generates a flow of (...)