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Open AccessArticle

Research on a Pedestrian Crossing Intention Recognition Model Based on Natural Observation Data

School of Automobile, Chang’an University, Xi’an 710064, China
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Sensors 2020, 20(6), 1776; https://doi.org/10.3390/s20061776 (registering DOI)
Received: 14 February 2020 / Revised: 19 March 2020 / Accepted: 20 March 2020 / Published: 23 March 2020
(This article belongs to the Special Issue Smart Sensors: Applications and Advances in Human Motion Analysis)
Accurate identification of pedestrian crossing intention is of great significance to the safe and efficient driving of future fully automated vehicles in the city. This paper focuses on pedestrian intention recognition on the basis of pedestrian detection and tracking. A large number of natural crossing sequence data of pedestrians and vehicles are first collected by a laser scanner and HD camera, then 1980 effective crossing samples of pedestrians are selected. Influencing parameter sets of pedestrian crossing intention are then obtained through statistical analysis. Finally, long short-term memory network with attention mechanism (AT-LSTM) model is proposed. Compared with the support vector machine (SVM) model, results show that when the pedestrian crossing intention is recognized 0 s prior to crossing, the recognition accuracy of the AT-LSTM model for pedestrian crossing intention is 96.15%, which is 6.07% higher than that of SVM model; when the pedestrian crossing intention is recognized 0.6 s prior, the recognition accuracy of AT-LSTM model is 90.68%, which is 4.85% higher than that of the SVM model. The determination of pedestrian crossing intention parameter set and the more accurate recognition of pedestrian intention provided in this work provide a foundation for future fully automated driving vehicles. View Full-Text
Keywords: natural observation data; pedestrian intention recognition; fully automated vehicle; intention parameter set; attention mechanism natural observation data; pedestrian intention recognition; fully automated vehicle; intention parameter set; attention mechanism
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Zhang, H.; Liu, Y.; Wang, C.; Fu, R.; Sun, Q.; Li, Z. Research on a Pedestrian Crossing Intention Recognition Model Based on Natural Observation Data. Sensors 2020, 20, 1776.

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