Zhang, C., Kalantari, A.H. orcid.org/0000-0001-5256-8069, Yang, Y. et al. (4 more authors) (2023) Cross or Wait? Predicting Pedestrian Interaction Outcomes at Unsignalized Crossings. In: 2023 IEEE Intelligent Vehicles Symposium (IV). 2023 IEEE Intelligent Vehicles Symposium (IV), 04-07 Jun 2023, Anchorage, AK, USA. IEEE ISBN 9798350346916
Abstract
Predicting pedestrian behavior when interacting with vehicles is one of the most critical challenges in the field of automated driving. Pedestrian crossing behavior is influenced by various interaction factors, including time to arrival, pedestrian waiting time, the presence of zebra crossing, and the properties and personality traits of both pedestrians and drivers. However, these factors have not been fully explored for use in predicting interaction outcomes. In this paper, we use machine learning to predict pedestrian crossing behavior including pedestrian crossing decision, crossing initiation time (CIT), and crossing duration (CD) when interacting with vehicles at unsignalized crossings. Distributed simulator data are utilized for predicting and analyzing the interaction factors. Compared with the logistic regression baseline model, our proposed neural network model improves the prediction accuracy and F1 score by 4.46% and 3.23%, respectively. Our model also reduces the root mean squared error (RMSE) for CIT and CD by 21.56% and 30.14% compared with the linear regression model. Additionally, we have analyzed the importance of interaction factors, and present the results of models using fewer factors. This provides information for model selection in different scenarios with limited input features.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | Pedestrian behavior prediction; machine learning; pedestrian-vehicle interaction; simulator study; automated driving |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Safety and Technology (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 14 Mar 2024 16:56 |
Last Modified: | 27 Mar 2024 15:19 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/iv55152.2023.10186616 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:210258 |