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<div class=""abs_img""><img src=""[disp_template_path]/JRM/abst-image/00270001/01.jpg"" width=""300"" />Risk potential estimation</div> Pedestrians darting out from blind spots in driver vision are typical scenarios in urban street environments, and conventional autonomous emergency braking systems reach safety limits if sensors do not detect the pedestrian in time to prevent accident or injury. The system must be able to anticipate such potential hazards and to anticipate such pedestrian action. This paper focuses on a pedestrian collision avoidance system that has a “driving-intelligence"" model. The model was designed by applying potential field theory using hazard-anticipatory knowledge. The effectiveness of the proposed system is confirmed by computer simulation. </span>

Elderly drivers often tend to disobey stop signs, and the number of vehicle accidents associated with this is increasing. The problem we address in this work is that of failure (or inability) of elderly drivers to identify potential conflicts with other road users at stop-sign intersections. The purpose of the study is to investigate the influence of deceleration control with brake hold on the driving habits of elderly drivers in potentially hazardous situations. This study proposes a driver assistance system with three functionalities: 1) information provision to warn drivers that they are approaching a stop-sign intersection, 2) deceleration control to stop the vehicle, and 3) brake hold to ensure that the vehicle has stopped completely. The timeline for a stop-sign intersection scenario is divided into pre-and postvehicle-stop phases. The effectiveness of the proposed approach in the context of braking-assistance intervention was evaluated by conducting a public-road driving experiment involving 34 elderly drivers. It was observed that the participants could be guided to exhibit rule-following driver behavior voluntarily. The proposed system increased safety for elderly drivers, not only by avoiding stop-sign violations, but also by increasing the available time for a driver to search for hidden hazards in blind spots. We conclude that the braking-assistance intervention system is effective in helping drivers avoid failure or inability to search for potential conflict owing to stop-sign violations.

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Detection of traversable areas is essential to navigation of autonomous personal mobility systems in unknown pedestrian environments. However, traffic rules may recommend or require driving in specified areas, such as sidewalks, in environments where roadways and sidewalks coexist. Therefore, it is necessary for such autonomous mobility systems to estimate the areas that are mechanically traversable and recommended by traffic rules and to navigate based on this estimation. In this paper, we propose a method for weakly-supervised recommended traversable area segmentation in environments with no edges using automatically labeled images based on paths selected by humans. This approach is based on the idea that a human-selected driving path more accurately reflects both mechanical traversability and human understanding of traffic rules and visual information. In addition, we propose a data augmentation method and a loss weighting method for detecting the appropriate recommended traversable area from a single human-selected path. Evaluation of the results showed that the proposed learning methods are effective for recommended traversable area detection and found that weakly-supervised semantic segmentation using human-selected path information is useful for recommended area detection in environments with no edges.