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The evaluation of pedestrian comfortability is important for the construction and management of walkable spaces. Pedestrian level-of-service (LOS), which is mostly categorized by density, has been widely applied and is believed to be capable of indicating the comfortability of the crowd. However, there is a lack of evidence that physically measured LOS reflects psychological comfortability, let alone the comparison between the performances of different candidate indicators, including the velocity and different local densities. Here, we show that walking velocity depicts pedestrian perceived congestion more accurately than density. In our experiments on pedestrians in a room-egress scenario with an inner obstacle, we obtained objective physical trajectory data from video analysis as well as subjective perception data from a questionnaire survey. The performance of the velocity and four types of local densities in reproducing pedestrian perceptions were numerically evaluated. We found that the velocity outperformed the local density. The lower performance of local densities was mainly caused by the pedestrians located at the back of the crowd, who walked at lower velocities and perceived higher congestion, despite their lower local densities that would correspond to less crowdedness from a physical viewpoint. Besides, the perceived congestion of pedestrians was shown to be affected by the initial pedestrian positions at the crowd, the obstacle layout, and the pedestrians’ age and gender. Furthermore, we suggest that the larger the gap between the desired and actual velocities, the larger the extent of the perceived congestion. We expect that our findings will contribute to a more accurate evaluation of pedestrian comfortability, which could help improve the walkable spaces of various infrastructures., source:https://doi.org/10.1016/j.trf.2022.04.007

Learning methods to adapt the planned path to individual drivers have been proposed to improve the comfort and trust provided by automated driving systems (ADSs). However, existing methods apply offline learning, even if they can accept the request to learn while driving (on-demand learning). Although several online learning methods are available, their learning results have not been applied in real-time for vehicle maneuvering. Focusing on obstacle avoidance, we propose on-demand online learning of preferred paths for individual drivers and investigate whether the proposed method improves the comfort and trust after learning. Unlike the existing methods, the proposed ADS can smoothly and arbitrarily transition between automated and manual driving, thereby learning preferred maneuvers whose results can be applied in real-time to obstacle avoidance. Accordingly, the proposed ADS includes mutual transfer of steering authority between the ADS and driver and curve modification according to the error between the planned path and actual vehicle trajectory. Experimental results show that the proposed ADS method improves the comfort and trust of drivers. In addition, the proposed ADS method learns the paths preferred by individual drivers during avoidance maneuvers to some extent and gradually adjusts the path according to the drivers preference through repeated learning., source:https://doi.org/10.1109/TIV.2022.3146622

The evaluation of pedestrian comfortability is important for the construction and management of walkable spaces. Pedestrian level-of-service (LOS), which is mostly categorized by density, has been widely applied and is believed to be capable of indicating the comfortability of the crowd. However, there is a lack of evidence that physically measured LOS reflects psychological comfortability, let alone the comparison between the performances of different candidate indicators, including the velocity and different local densities. Here, we show that walking velocity depicts pedestrian perceived congestion more accurately than density. In our experiments on pedestrians in a room-egress scenario with an inner obstacle, we obtained objective physical trajectory data from video analysis as well as subjective perception data from a questionnaire survey. The performance of the velocity and four types of local densities in reproducing pedestrian perceptions were numerically evaluated. We found that the velocity outperformed the local density. The lower performance of local densities was mainly caused by the pedestrians located at the back of the crowd, who walked at lower velocities and perceived higher congestion, despite their lower local densities that would correspond to less crowdedness from a physical viewpoint. Besides, the perceived congestion of pedestrians was shown to be affected by the initial pedestrian positions at the crowd, the obstacle layout, and the pedestrians’ age and gender. Furthermore, we suggest that the larger the gap between the desired and actual velocities, the larger the extent of the perceived congestion. We expect that our findings will contribute to a more accurate evaluation of pedestrian comfortability, which could help improve the walkable spaces of various infrastructures., source:https://doi.org/10.1016/j.trf.2022.04.007

Learning methods to adapt the planned path to individual drivers have been proposed to improve the comfort and trust provided by automated driving systems (ADSs). However, existing methods apply offline learning, even if they can accept the request to learn while driving (on-demand learning). Although several online learning methods are available, their learning results have not been applied in real-time for vehicle maneuvering. Focusing on obstacle avoidance, we propose on-demand online learning of preferred paths for individual drivers and investigate whether the proposed method improves the comfort and trust after learning. Unlike the existing methods, the proposed ADS can smoothly and arbitrarily transition between automated and manual driving, thereby learning preferred maneuvers whose results can be applied in real-time to obstacle avoidance. Accordingly, the proposed ADS includes mutual transfer of steering authority between the ADS and driver and curve modification according to the error between the planned path and actual vehicle trajectory. Experimental results show that the proposed ADS method improves the comfort and trust of drivers. In addition, the proposed ADS method learns the paths preferred by individual drivers during avoidance maneuvers to some extent and gradually adjusts the path according to the drivers preference through repeated learning., source:https://doi.org/10.1109/TIV.2022.3146622