Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7148

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Subject: search.filters.subject.Contrastive representation learning

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  • Conference Object
    Citation - Scopus: 2
    Lane Change Detection With an Ensemble of Image-Based and Video-Based Deep Learning Models
    (Institute of Electrical and Electronics Engineers Inc., 2023) Nalcakan, Y.; Baştanlar, Yalın; Bastanlar, Y.; 03.04. Department of Computer Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    Prediction of lane-changing maneuvers of surrounding vehicles is important for autonomous vehicles to understand the scene properly. This research proposes a vision-based technique that only requires a single in-car RGB camera. The surrounding vehicles' maneuvers are classified as right/left lane-change or no lane change conforming to most lane change detection studies in the literature. The usual practice in previous studies is feeding individual video frames into CNN to extract features and afterward using an LSTM to classify the sequence of features. Differently, in our study, we exploit the power of ensembling the prediction results of two methods. The first one uses a small feature vector containing the image coordinates of the target vehicle and classifies it with an LSTM. The second method works with a simplified scene representation video (only the target vehicle and ego-lane highlighted) and it is based on a self-supervised contrastive video representation learning scheme. Since maneuver labeling is not required in the self-supervised learning step this enables the use of a relatively large dataset. After the self-supervised training, the model is fine-tuned with a labeled dataset. Our experimental study on a well-known lane change detection dataset reveals that both of the mentioned methods by themselves achieve state-of-the-art results and ensembling them increases the classification accuracy even more. © 2023 IEEE.
  • Article
    Citation - Scopus: 3
    Cut-In Maneuver Detection With Self-Supervised Contrastive Video Representation Learning
    (Springer, 2023) Nalçakan, Yağız; Baştanlar, Yalın; Baştanlar, Yalın; Nalçakan, Yağız; 03.04. Department of Computer Engineering; 03. Faculty of Engineering; 01. Izmir Institute of Technology
    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%