We measured the lowest velocity (velocity threshold) for discriminating motion direction in relative and uniform motion stimuli, varying the contrast and the spatial frequency of the stimulus gratings. The results showed significant differences in the effects of contrast and spatial frequency on the threshold, as well as on the absolute threshold level between the two motion conditions, except when the contrast was 1% or lower. Little effect of spatial frequency was found for uniform motion, whereas a bandpass property with a peak at approximately 5 cycles per degree was found for relative motion. It was also found that contrast had little effect on uniform motion, whereas the threshold decreased with increases in contrast up to 85% for relative motion. These differences cannot be attributed to possible differences in eye movements between the relative and the uniform motion conditions, because the spatial-frequency characteristics differed in the two conditions even when the presentation duration was short enough to prevent eye movements. The differences also cannot be attributed to detecting positional changes, because the velocity threshold was not determined by the total distance of the stimulus movements. These results suggest that there are two different motion pathways: one that specializes in relative motion and one that specializes in uniform or global motion. A simulation showed that the difference in the response functions of the two possible pathways accounts for the differences in the spatial-frequency and contrast dependency of the velocity threshold.

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Localized torrential rainfall events and related traffic problems are increasing in Japan, suggesting the need for a navigation-alert system to help drivers avoid such risks. Based on ongoing developments of weather radar systems for early detection of localized torrential rain and a cross-data collaboration platform for traffic optimization, in this study we tested the application of a route-guidance system that can help drivers avoid heavy rainfall. Participants were given equivalent levels of pre-training un the early detection of rainfall and the relationship between rainfall and accidents, then allowed to test a driving simulator set up with four alert methods, three route options, and four levels of possible risk avoidance. Using this system, the heavy rain avoidance rate was 85.63%, suggesting that such a system would be socially acceptable and useful, though further research is needed to refine the specific approach.

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Car drivers select their routes based on the information obtained about accidents and traffic congestion along the route. In recent years, nowcasting and forecasting of various traffic risk events is being performed by using diverse sensor data. However, there is no clarity as yet on what and how to communicate to the driver in case there are traffic risks on the route. In this paper, we have developed an environment that enables non UI experts to quickly create car navigation prototypes by using traffic risk data. This paper includes our report on a hackathon that we held using this environment. The hackathon theme was "Develop a new car navigation system equipped with a mechanism that makes the driver aware of traffic risks and helps them determine the most appropriate driving routes." Twenty three researchers and professionals from the field of traffic engineering participated. Our results have brought certain common problems to the awareness of the experts. The information obtained from this report will be very beneficial for our community to determine the direction of collaboration. CCS Information systems~Mobile information processing systemsHuman-centered computing~Field studies Human-centered computing~Interface design prototyping