Pedestrian Path, Pose, and Intention Prediction Through Gaussian Process Dynamical Models and Pedestrian Activity Recognition

According to several reports published by worldwide organizations, thousands of pedestrians die in road accidents every year. Due to this fact, vehicular technologies have been evolving with the intent of reducing these fatalities. This evolution has not finished yet, since, for instance, the predictions of pedestrian paths could improve the current automatic emergency braking systems. For this reason, this paper proposes a method to predict future pedestrian paths, poses, and intentions up to 1 s in advance. This method is based on balanced Gaussian process dynamical models (B-GPDMs), which reduce the 3-D time-related information extracted from key points or joints placed along pedestrian bodies into low-dimensional spaces. The B-GPDM is also capable of inferring future latent positions and reconstruct their associated observations. However, learning a generic model for all kinds of pedestrian activities normally provides less accurate predictions. For this reason, the proposed method obtains multiple models of four types of activity, i.e., walking, stopping, starting, and standing, and selects the most similar model to estimate future pedestrian states. This method detects starting activities 125 ms after the gait initiation with an accuracy of 80% and recognizes stopping intentions 58.33 ms before the event with an accuracy of 70%. Concerning the path prediction, the mean error for stopping activities at a time-to-event (TTE) of 1 s is 238.01 ± 206.93 mm and, for starting actions, the mean error at a TTE of 0 s is 331.93 ± 254.73 mm.