Shinichiro Sega

Qualitative Reasoning Approach to a Driver's Cognitive Mental Load

This paper explores applying qualitative reasoning to a drivers mental state in real driving situations so as to develop a working load for intelligent transportation systems. The authors identify the cognitive state that determines whether a driver will be ready to operate a device in car navigation. In order to identify the drivers cognitive state, the authors will measure eye movements during car-driving situations. Data can be acquired for the various actions of a car driver, in particular braking, acceleration, and steering angles from the experiment car. The authors constructed a driver cognitive mental load using the framework of qualitative reasoning. The response of the model was checked by qualitative simulation. The authors also verified the model using real data collected by driving an actual car. The results indicated that the model could represent the change in the cognitive mental load based on measurable data. This means that the framework of this paper will be useful for designing user interfaces for next-generation systems that actively employ user situations.

Applying qualitative reasoning to a driver's cognitive mental load

In this paper, we explore applying qualitative reasoning to a drivers mental state in real driving situations so as to develop a working load for intelligent transportation systems. We identify the cognitive state that determines whether a driver will be ready to operate a device in car navigation. In order to identify the drivers cognitive state, we will measure eye movements during car-driving situations. We can acquire data for the various actions of a car driver, in particular braking, acceleration, and steering angles from our experiment car. We constructed a driver cognitive mental load using the framework of qualitative reasoning. We checked the response of our model by qualitative simulation. We also verified the model using real data collected by driving an actual car. The results indicated that our model could represent the change in the cognitive mental load based on measurable data. This means that the framework of this paper will be useful for designing user interfaces for next-generation systems that actively employ user situations.

Personal DJ cloud: That can provide programs suitable for user preference and cognitive situations for a smart phone

In this paper, we have described our Personal DJ Cloud (PDJC), which can provide sequence of contents (Program) suitable for user preference and cognitive situations for a smart phone. The program is in combination with music contents, Internet radio, podcasts and text-to-speech (TTS) of Really Simple Syndication (RSS) contents. Bayesian Network estimates user preference and situations and Constraint Satisfaction generates practical programs in which they are reflected. And the cloud computing allows PDJC to gather Internet contents suitable for user preference efficiently. Through our user evaluation, we clarified that our PDJC can reduce the number of users operations and generate programs of higher degree of satisfaction. So, PDJC has proposed the framework of applications design of considering with user preference and users cognitive situations in use of smart phone.