Track-Based Forecasting of Pedestrian Behavior by Polynomial Approximation and Multilayer Perceptrons

  • We present an approach for predicting continuous pedestrian trajectories over a time horizon of 2.5 s by means of polynomial least squares approximation and multilayer perceptron (MLP) artificial neural networks. The training data are gathered from 1075 real urban traffic scenes with uninstructed pedestrians including starting, stopping, walking and bending in. The polynomial approximation provides an extraction of the principal information of the underlying time series in the form of the polynomial coefficients. It is independent of sensor parameters such as cycle time and robust regarding noise. Approximation and prediction can be performed very efficiently. It only takes 35 ms on an Intel Core i7 CPU. Test results show 28% lower prediction errors for starting scenes and 32% for stopping scenes in comparison to applying a constant velocity movement model. Approaches based on MLP without polynomial input or Support Vector Regression (SVR) models as motion predictor are outperformed as well.
Metadaten
Author:Michael Goldhammer, Sebastian Köhler, Konrad Doll, Bernhard Sick
ISBN:978-3-319-33386-1
Parent Title (English):Intelligent Systems and Applications - Extended and Selected Results from the SAI Intelligent Systems Conference (IntelliSys) 2015
Publisher:Springer International Publishing
Document Type:Conference Proceeding
Language:English
Year of Completion:2016
Release Date:2016/05/09
GND Keyword:Fußgänger; Verkehrsverhalten; Prognose
First Page:259
Last Page:279
Urheberrecht:1
research focus :Intelligent Systems / Artifical Intelligence and Data Science
Intelligent Systems / Intelligent Mobility
Intelligent Systems / Intelligent Sensors and Signals
Licence (German):Keine Lizenz - es gilt das deutsche Urheberrecht
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