Please use this identifier to cite or link to this item:
https://hdl.handle.net/11147/13220
Title: | Cut-in maneuver detection with self-supervised contrastive video representation learning | Authors: | Nalçakan, Yağız Baştanlar, Yalın |
Keywords: | Contrastive representation learning Driver assistance systems Vehicle maneuver classification |
Publisher: | Springer | Abstract: | The detection of the maneuvers of the surrounding vehicles is important for autonomous vehicles to act accordingly to avoid possible accidents. This study proposes a framework based on contrastive representation learning to detect potentially dangerous cut-in maneuvers that can happen in front of the ego vehicle. First, the encoder network is trained in a self-supervised fashion with contrastive loss where two augmented videos of the same video clip stay close to each other in the embedding space, while augmentations from different videos stay far apart. Since no maneuver labeling is required in this step, a relatively large dataset can be used. After this self-supervised training, the encoder is fine-tuned with our cut-in/lane-pass labeled datasets. Instead of using original video frames, we simplified the scene by highlighting surrounding vehicles and ego-lane. We have investigated the use of several classification heads, augmentation types, and scene simplification alternatives. The most successful model outperforms the best fully supervised model by ∼ 2% with an accuracy of 92.52% | URI: | https://doi.org/10.1007/s11760-023-02512-3 https://hdl.handle.net/11147/13220 |
ISSN: | 1863-1703 |
Appears in Collections: | Computer Engineering / Bilgisayar Mühendisliği Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection |
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File | Description | Size | Format | |
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s11760-023-02512-3.pdf Until 2025-07-01 | Article | 1.78 MB | Adobe PDF | View/Open Request a copy |
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