Please use this identifier to cite or link to this item: https://hdl.handle.net/11147/14048
Title: Monocular vision-based prediction of cut-in manoeuvres with LSTM networks
Authors: Nalçakan, Yağız
Baştanlar, Yalın
Keywords: Driver assistance systems
Maneuver prediction
Vehicle behavior prediction
Automobile drivers
Issue Date: 2023
Publisher: Springer
Abstract: Advanced driver assistance and automated driving systems should be capable of predicting and avoiding dangerous situations. In this paper, we first discuss the importance of predicting dangerous lane changes and provide its description as a machine learning problem. After summarizing the previous work, we propose a method to predict potentially dangerous lane changes (cut-ins) of the vehicles in front. We follow a computer vision-based approach that only employs a single in-vehicle RGB camera, and we classify the target vehicle’s maneuver based on the recent video frames. Our algorithm consists of a CNN-based vehicle detection and tracking step and an LSTM-based maneuver classification step. It is computationally efficient compared to other vision-based methods since it exploits a small number of features for the classification step rather than feeding CNNs with RGB frames. We evaluated our approach on a publicly available driving dataset and a lane change detection dataset. We obtained 0.9585 accuracy with the side-aware two-class (cut-in vs. lane-pass) classification model. Experiment results also reveal that our approach outperforms state-of-the-art approaches when used for lane change detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
Description: Science, Engineering Management and Information Technology First International Conference, SEMIT 2022 -- 2 February 2022 through 3 February 2022
URI: https://doi.org/10.1007/978-3-031-40395-8_8
https://hdl.handle.net/11147/14048
ISBN: 9783031403941
ISSN: 1865-0929
Appears in Collections:Computer Engineering / Bilgisayar Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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