Nalçakan, YağızBaştanlar, Yalın2023-11-112023-11-11202397830314039411865-0929https://doi.org/10.1007/978-3-031-40395-8_8https://hdl.handle.net/11147/14048Science, Engineering Management and Information Technology First International Conference, SEMIT 2022 -- 2 February 2022 through 3 February 2022Advanced 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.eninfo:eu-repo/semantics/closedAccessDriver assistance systemsManeuver predictionVehicle behavior predictionAutomobile driversConference Object2-s2.0-8517273216210.1007/978-3-031-40395-8_8