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Nalçakan, Yağız
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03.04. Department of Computer Engineering
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4
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5
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| Journal | Count |
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| Communications in Computer and Information Science | 1 |
| Lecture Notes in Networks and Systems | 1 |
| Signal Image and Video Processing | 1 |
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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.Article Citation - Scopus: 3Cut-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 TechnologyThe 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%Doctoral Thesis Classification of Maneuvers of Vehicles in Front for Driver Assistance Systems(01. Izmir Institute of Technology, 2023) Baştanlar, Yalın; Nalçakan, Yağız; Baştanlar, Yalın; 03.04. Department of Computer Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyPredicting vehicle maneuvers is a critical task for developing autonomous driving. These maneuvers have been identified as leading causes of fatal accidents, underscoring the need for robust and reliable detection systems. This thesis addresses this critical issue by developing and evaluating novel methodologies for classifying maneuvers, especially lane change and cut-in maneuvers in front of the vehicle. Two specific methods are proposed in this thesis work, and their effectiveness is evaluated on two datasets: the Prevention Lane Change Prediction dataset and the BDD-100K Cut-in/Lane-pass Classification Subset. The first method is a model that utilizes features extracted from the bounding boxes of the target vehicle, feeding them into a single-layer LSTM network for cut-in/lane-pass classification. The second method involves training a 3-dimensional residual neural network in a self-supervised manner using contrastive video representation learning. For the self-supervised training phase, a novel scene representation is proposed to highlight vehicle motions. Afterward, the same model is fine-tuned using labeled video data. Lastly, an ensemble learning approach is introduced, which combines the predictive capabilities of the proposed LSTM-based and self-supervised contrastive video representation learning models, leveraging the strengths of both methods to enhance the overall maneuver classification performance. The proposed methods made significant contributions to the field. The LSTM-based model achieved high classification accuracies compared to other studies in the literature. The self-supervised video representation learning model represents the first application of contrastive learning in maneuver classification. The ensemble learning approach has shown a significant improvement in the performance of the maneuver detection system.Conference Object Citation - Scopus: 1A Novel Feature To Predict Buggy Changes in a Software System(Springer, 2022) Yılmaz, Rahime; Nalçakan, Yağız; Nalçakan, Yağız; Haktanır, Elif; 03.04. Department of Computer Engineering; 03. Faculty of Engineering; 01. Izmir Institute of TechnologyResearchers have successfully implemented machine learning classifiers to predict bugs in a change file for years. Change classification focuses on determining if a new software change is clean or buggy. In the literature, several bug prediction methods at change level have been proposed to improve software reliability. This paper proposes a model for classification-based bug prediction model. Four supervised machine learning classifiers (Support Vector Machine, Decision Tree, Random Forrest, and Naive Bayes) are applied to predict the bugs in software changes, and performance of these four classifiers are characterized. We considered a public dataset and downloaded the corresponding source code and its metrics. Thereafter, we produced new software metrics by analyzing source code at class level and unified these metrics with the existing set. We obtained new dataset to apply machine learning algorithms and compared the bug prediction accuracy of the newly defined metrics. Results showed that our merged dataset is practical for bug prediction based experiments. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.