Nandhini N and Siva Sangari M, Department of Computer Science and Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamilnadu, India.
Online First : 30 December 2020
Publisher Name : IJAICT India Publications, India.
Print ISBN : 978-81-950008-0-7
Online ISBN : 978-81-950008-1-4
Page :128-132
Abstract
Walking is the special mode of transportation that comprises 60% of the World’s population. This population includes the pedestrians as the focus of sight, because they are considered as the road users. Understanding the behavior of road users is a difficult task as people have complex mindsets. When people are crossing the road, they have been subjected to various attitude issues and other psychological issues. The prediction of the pedestrian’s motion is very ridiculous, because their motions has to be predicted related to the traffic parameter. A prediction algorithm always uses the past experiences to anticipate the future events. These algorithms use the set of sample data from a sample collected over a period. This makes prediction algorithms more ideal to use within smart environments. So as a part of this study, we have compared various algorithms, for their accurateness of the prediction on pedestrians’ movements. This paper also focuses on the various pedestrian’s data set, pedestrian behavior estimation and the various algorithms used to predict the behavior of the people. As a part of our research on pedestrian safety prediction would be done by providing an efficient prediction algorithm based on their movements, ecological factors, pedestrians’ psychological factors, other outreaches.
Keywords
Prediction algorithm, Pedestrians, Safety, SVM, KF.
Cite this article
Nandhini N and Siva Sangari M, “A Survey on Predicting Pedestrian Safety based on Physical and Psychological factors of Pedestrians Motion”, Innovations in Information and Communication Technology, pp. 128-132, December 2020.
Copyright
© 2020 Nandhini N and Siva Sangari M. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
