Driving assistance systems that adapt to an individual driver are essential for avoiding traffic accidents because there are individual differences in the way of driving. To realize such systems, it is necessary to take account of not only observable physical quantities, but also information inferred from observation. For example, a collision avoidance system warns a driver according to an esti...

The establishment of desirable social norms is an integral part of a well-functioning civil society. While recent evidence has demonstrated that social comparison can affect behavior in a variety of contexts, it is not clear what type of comparative social information is most effective. Using a largescale field experiment to study driving practices, we sent text messages containing different ty...

Abstract: Driving assistance systems are essential technologies to avoid traffic accidents, reduce traffic jams, and solve environmental problems. Not only observable behavioral data, but also unobservable inferred values should be considered to realize advanced driving assistance systems that are adaptable to individual drivers and situations. For this purpose, Bayesian networks, which are the...

OBJECTIVE The purpose of the present study was to examine relationships, if any, between the number of lifetime driving citations and the number of lifetime criminal charges. METHODS Using a cross-sectional, consecutive sample of internal medicine outpatients and a self-report survey methodology, we queried participants about the number of past driving citations as well as charges for any of ...

In this paper we present a model for autonomous driving behavior useful for creating ambient traac as well as experiment speciic scenarios for driving simulation. This model follows roadways, obeying the rules of the road. It reacts to nearby vehicles and traac control devices. The model supports a range of behaviors including passing, lane changes, and safe navigation through intersections. Th...