论文标题
通过高斯过程动态模型和行人活动识别的行人路径,姿势和意图预测
Pedestrian Path, Pose and Intention Prediction through Gaussian Process Dynamical Models and Pedestrian Activity Recognition
论文作者
论文摘要
根据全球组织发表的几份报告,每年成千上万的行人死于道路事故。由于这一事实,车辆技术一直在发展,目的是减少这些死亡。例如,这种发展尚未完成,例如,行人路径的预测可以改善当前的自动紧急制动系统(AEB)。因此,本文提出了一种预测未来行人路径,姿势和意图的方法。该方法基于平衡的高斯工艺动力学模型(B-GPDM),该模型减少了从沿着行人体放置的关节或关节提取的3D时间相关信息,使其转变为低维空间。 B-GPDM还能够推断未来的潜在位置并重建其相关的观察结果。但是,学习各种行人活动的通用模型通常会提供较少的预测。因此,所提出的方法获得了多种活动的多种模型,即步行,停止,开始和站立,并选择最相似的模型来估计未来的行人状态。该方法以80%的精度检测步态启动后的125毫秒开始活动,并在事件发生前以70%的精度识别出意图58.33毫秒。关于路径预测,在事件时间(TTE)(TTE)中停止活动的平均误差为238.01mm,对于启动操作,在0S的启动操作中,平均误差为331.93mm。
According to several reports published by worldwide organisations, 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 (AEBS). For this reason, this paper proposes a method to predict future pedestrian paths, poses and intentions up to 1s in advance. This method is based on Balanced Gaussian Process Dynamical Models (B-GPDMs), which reduce the 3D time-related information extracted from keypoints 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 kind of pedestrian activities normally provides less ccurate 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 125ms after the gait initiation with an accuracy of 80% and recognises stopping intentions 58.33ms 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 1s is 238.01mm and, for starting actions, the mean error at a TTE of 0s is 331.93mm.