Noboru Kiyama

A route search method for electric vehicles in consideration of range and locations of charging stations

In this paper, we propose a new route search method for electric vehicles (EVs), which calculates a route with stopping over charging stations to have extra battery charge when current remaining charge is not enough to reach the destination. In this method, firstly we divided cases into three; the case of reaching the destination straight from the departure point, the case of stopping at one charging station and the case of stopping at several charging stations. In the cases of stopping over at least one charging station, potential charging stations are selected first, and then a route is obtained by Dijkstras algorithm from routes with some of those stations. Travel distance, estimated travel time between charge stations and time to fully charge after the EVs arrival at each charging stations are calculated and also used for the calculation of travel cost between charging stations. Offering these routes for EV users will eliminate some of the major concerns about EVs such as limited range or sparse charging stations and will contribute to the spread of EVs. In order to confirm appropriateness of the route and examine the impact of increased execution time by this method, authors implemented the method and compared its execution time with that of conventional route search method. As a result, we confirmed that this method calculates routes with accessible charging stations according to the EVs range. We also found that the search of charging stations with this method requires extra execution time, but the time increases by less than a half of the time required for the search of the route with some of these stations by conventional method.

Quantifying relationship between relative position error of localization algorithms and object identification

Positioning of things, devices and people is the fundamental technology in ubiquitous computing. However, few literature has discussed the impact of positioning errors due to localization algorithm properties such as ranging noise and deployment of anchors on people’s identification of objects. Since several factors such as relative distance, relative angles and grouping of objects are intricately related with each other in such identification, it is not an easy task to investigate its characteristics. In this paper, we propose criteria to assess the “accuracy” of the estimated positions in identifying the objects. The criteria are helpful to design, develop and evaluate localization algorithms that are used to tell people the location of objects. Augmented reality is a typical example that needs such localization algorithms. To model the criteria without ambiguity, we prove that the Delaunay triangulation well-captures natural human behavior of finding similarity between estimated and true positions. We have examined different localization algorithms to observe how the proposed model quantifies the properties of those algorithms. Subjective testing has also been conducted using questionnaires to justify our quantification sufficiently renders human intuition.