论文标题
使用LSTM网络基于单眼视力预测
Monocular Vision-based Prediction of Cut-in Maneuvers with LSTM Networks
论文作者
论文摘要
高级驾驶员协助和自动驾驶系统应能够预测和避免危险情况。这项研究提出了一种预测自我车道中可能发生的潜在危险切割动作的方法。我们遵循一种基于计算机视觉的方法，该方法仅采用单个车载RGB摄像头，并根据最近的视频帧对目标车辆的操作进行分类。我们的算法包括一个基于CNN的车辆检测和跟踪步骤以及基于LSTM的机动分类步骤。它比其他基于视觉的方法更有效，因为它利用了分类步骤的少数功能，而不是用RGB帧喂食CNN。我们在公开可用的驾驶数据集和车道更改检测数据集上评估了我们的方法。我们获得了0.9585的精度，具有侧感的两级（切入与通道）分类模型。实验结果还表明，用于车道更改检测时，我们的方法的表现要优于最先进的方法。
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.
