Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Conference Object Citation - Scopus: 2Lane 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 TechnologyPrediction 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.Conference Object Citation - Scopus: 1Monocular Vision-Based Prediction of Cut-In Manoeuvres With Lstm Networks(Springer, 2023) Nalçakan, Yağız; Baştanlar, Yalın; 03.04. Department of Computer Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyAdvanced 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.