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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Vydáno v:	Transportation research. Part C, Emerging technologies Ročník 133; s. 103414
Hlavní autoři:	Shi, Qian, Zhang, Hui
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 01.12.2021
Témata:	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
On-line přístup:	Získat plný text
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