We present the pedestrian patterns dataset for autonomous driving. The
dataset was collected by repeatedly traversing the same three routes for one
week starting at different specific timeslots. The purpose of the dataset is to
capture the patterns of social and pedestrian behavior along the traversed
routes at different times and to eventually use this information to make
predictions about the risk associated with autonomously traveling along
different routes. This dataset contains the Full HD videos and GPS data for
each traversal. Fast R-CNN pedestrian detection method is applied to the
captured videos to count the number of pedestrians at each video frame in order
to assess the density of pedestrians along a route. By providing this
large-scale dataset to researchers, we hope to accelerate autonomous driving
research not only to estimate the risk, both to the public and to the
autonomous vehicle but also accelerate research on long-term vision-based
localization of mobile robots and autonomous vehicles of the future.

介绍了一份用于自动驾驶的行人模式数据集，其中包含了行人和车辆的 Full HD 视频和 GPS 数据，通过对采集的视频进行快速 R-CNN 行人检测，可得出每个时间段内人流密度估计，以便于评估不同路径下的人流风险。