Navigation systems providing route-guidance and traffic information are one of the most widely used driver-support systems these days. Most navigation systems are based on the map paradigm which plots the driving route in an abstracted version of a two-dimensional electronic map. Recently, a new navigation paradigm was introduced that is based on the augmented reality (AR) paradigm which displays the driving route by superimposing virtual objects on the real scene. These two paradigms have their own innate characteristics from the point of human cognition, and so complement each other rather than compete with each other. Regardless of the paradigm, the role of any navigation system is to support the driver in achieving his driving goals. The objective of this work is to investigate how these map and AR navigation paradigms impact the achievement of the driving goals: productivity and safety. We performed comparative experiments using a driving simulator and computers with 38 subjects. For the effects on productivity, driver's performance on three levels (control level, tactical level, and strategic level) of driving tasks was measured for each map and AR navigation condition. For the effects on safety, driver's situation awareness of safety-related events on the road was measured. To find how these navigation paradigms impose visual cognitive workload on driver, we tracked driver's eye movements. As a special factor of driving performance, route decision making at the complex decision points such as junction, overpass, and underpass was investigated additionally. Participant's subjective workload was assessed using the Driving Activity Load Index (DALI). Results indicated that there was little difference between the two navigation paradigms on driving performance. AR navigation attracted driver's visual attention more frequently than map navigation and then reduces awareness of and proper action for the safety-related events. AR navigation was faster and better to support route decision making at the complex decision points. According to the subjective workload assessment, AR navigation was visually and temporally more demanding.

Mobile devices, such as cellular phones and car navigation systems, are essential to daily life. People acquire necessary information and preferred content over communication networks anywhere, anytime. However, usability issues arise from the simplicity of user interfaces themselves. Thus, a recommendation of content that is adapted to a user's preference and situation will help the user select content. In this paper, we describe a method to realize such a system using Bayesian networks. This user-adapted mobile system is based on a user model that provides recommendation of content (i.e., restaurants, shops, and music that are suitable to the user and situation) and that learns incrementally based on accumulated usage history data. However, sufficient samples are not always guaranteed, since a user model would require combined dependency among users, situations, and contents. Therefore, we propose the LK method for modeling, which complements incomplete and insufficient samples using knowledge data, and CPT incremental learning for adaptation based on a small number of samples. In order to evaluate the methods proposed, we applied them to restaurant recommendations made on car navigation systems. The evaluation results confirmed that our model based on the LK method can be expected to provide better generalization performance than that of the conventional method. Furthermore, our system would require much less operation than current car navigation systems from the beginning of use. Our evaluation results also indicate that learning a user's individual preference through CPT incremental learning would be beneficial to many users, even with only a few samples. As a result, we have developed the technology of a system that becomes more adapted to a user the more it is used.