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Lane-GNN: integrating GNN for predicting drivers’ lane change intention

Wu, Hongde and Liu, Mingming orcid logoORCID: 0000-0002-8988-2104 (2022) Lane-GNN: integrating GNN for predicting drivers’ lane change intention. In: 25th IEEE International Conference on Intelligent Transportation Systems (IEEE ITSC 2022), 8-12 Oct 2022, Macau, China.

Abstract
Nowadays, intelligent highway traffic network is playing an important role in modern transportation infrastructures. A variable speed limit (VSL) system can be facilitated in the highway traffic network to provide useful and dynamic speed limit information for drivers to travel with enhanced safety. Such system is usually designed with a steady advisory speed in mind so that traffic can move smoothly when drivers follow the speed, rather than speeding up whenever there is a gap and slowing down at congestion. However, little attention has been given to the research of vehicles’ behaviours when drivers left the road network governed by a VSL system, which may largely involve unexpected acceleration, deceleration and frequent lane changes, resulting in chaos for the subsequent highway road users. In this paper, we focus on the detection of traffic flow anomaly due to drivers’ lane change intention on the highway traffic networks after a VSL system. More specifically, we apply graph modelling on the traffic flow data generated by a popular mobility simulator, SUMO, at road segment levels. We then evaluate the performance of lane changing detection using the proposed Lane-GNN scheme, an attention temporal graph convolutional neural network, and compare its performance with a temporal convolutional neural network (TCNN) as our baseline. Our experimental results show that the proposed Lane-GNN can detect drivers’ lane change intention within 90 seconds with an accuracy of 99.42% under certain assumptions. Finally, some interpretation methods are applied to the trained models with a view to further illustrate our findings.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Subjects:Computer Science > Artificial intelligence
Computer Science > Machine learning
Engineering > Systems engineering
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Electronic Engineering
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Published in: 25th IEEE International Conference on Intelligent Transportation Systems (IEEE ITSC 2022), Proceedings. . IEEE.
Publisher:IEEE
Official URL:https://doi.org/10.1109/ITSC55140.2022.9922139
Copyright Information:© 2022 The Authors
Funders:Science Foundation Ireland Grant Number SFI/12/RC/2289 P2, European Regional Development Fund, Research master scholarship funded by the school of electronic engineering, faculty of engineering and computing at DCU.
ID Code:27339
Deposited On:10 Oct 2022 09:57 by Mingming Liu . Last Modified 14 Feb 2023 11:40
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