Visual considerations of relative trajectory models of moving objects
Kazuo Misue, Kosuke Otake, Takahiro Saito · 2017
A promising approach for preventing collision accidents between moving objects is to automatically detect a situation where the status of the objects is anomalous. To exploit machine learning techniques for detecting anomaly status, we should prepare suitable models to express the statuses of moving objects in advance. In this paper, several variations of vector models are introduced. These vector models express the relative trajectories of two moving objects approaching each other. To investigate the features of the vector models, the authors tried to observe them by using panel matrices. By means of visualizations using a panel matrix, we can easily grasp the dependence of vector models to parameters and the similarity among variations of the models.