An improved learning-based LSTM approach for lane change intention prediction subject to imbalanced data

Lane change intention prediction is an essential component for motion planning of Autonomous Vehicles (AVs). In this work, we aim to achieve this task by using Long-Short Term Memory (LSTM) network. One critical challenge on this task is that the dataset used for training such a network is usually h...

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Bibliographic Details

Published in:	Transportation research. Part C, Emerging technologies Vol. 133; p. 103414
Main Authors:	Shi, Qian, Zhang, Hui
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 01.12.2021
Subjects:	Autonomous Vehicles Data imbalance Ensemble learning Lane change intention prediction Long Short Term Memory (LSTM) network Over-sampling method Autonomous Vehicles Ensemble learning Lane change intention prediction Long Short Term Memory (LSTM) network Over-sampling method Data imbalance
ISSN:	0968-090X, 1879-2359
Online Access:	Get full text
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