Monocular Vision-based Prediction of Cut-in Maneuvers with LSTM Networks

• URL: http://arxiv.org/abs/2203.10707v1
• Date: Mon, 21 Mar 2022 02:30:36 GMT
• Title: Monocular Vision-based Prediction of Cut-in Maneuvers with LSTM Networks
• Authors: Yagiz Nalcakan and Yalin Bastanlar
• Abstract summary: This study proposes a method to predict potentially dangerous cut-in maneuvers happening in the ego lane. We follow a computer vision-based approach that only employs a single in-vehicle RGB camera. Our algorithm consists of a CNN-based vehicle detection and tracking step and an LSTM-based maneuver classification step.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Advanced driver assistance and automated driving systems should be capable of predicting and avoiding dangerous situations. This study proposes a method to predict potentially dangerous cut-in maneuvers happening in the ego lane. 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 more computationally efficient than 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 side-aware two-class (cut-in vs. lane-pass) classification models. Experiment results also reveal that our approach outperforms state-of-the-art approaches when used for lane change detection.

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