NDLI: Improved multi-level pedestrian behavior prediction based on matching with classified motion patterns

Content Provider	IEEE Xplore Digital Library
Author	Zhuo Chen Yung, N.H.C.
Copyright Year	2009
Description	Author affiliation: Department of Electrical and Electronic Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, H.K.S.A.R. (Zhuo Chen; Yung, N.H.C.)
Abstract	This paper proposes an improved multi-level pedestrian behavior prediction method based on our previous research work on learning pedestrian motion patterns and predicting pedestrian long-term behaviors as their motion instances are being observed. The improvement mainly focuses on the similarity matching criteria between the trajectory and the clustered MP whose main advantages are that (1) a reasonable similarity range of MP is automatically calculated instead of manually set; (2) the distance feature and the changing angle feature are considered together for similarity matching while only the distance feature is considered before. The improved method has been implemented and a study of how the new prediction method performs in real world scenario is conducted. The results show that it works well in real DCE and the prediction is consistent with the actual behavior.
Starting Page	1
Ending Page	6
File Size	1671346
Page Count	6
File Format	PDF
ISBN	9781424455195
DOI	10.1109/ITSC.2009.5309849
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-04
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pattern matching Prediction methods Road accidents Intelligent transportation systems Road vehicles Navigation USA Councils Path planning Clustering algorithms Trajectory dynamically changing environment Motion patterns similarity matching multi-level behavior prediction
Content Type	Text
Resource Type	Article

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