Du Luyao, Chen Wei, Pei Zhonghui, Zheng Hongjiang, Fu Shuaizhi, Chen Kang, Wu Di
School of Automation, Wuhan University of Technology, Wuhan 430070, China.
School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China.
Comput Intell Neurosci. 2020 Sep 30;2020:8848363. doi: 10.1155/2020/8848363. eCollection 2020.
Detection of lane-change behaviour is critical to driving safety, especially on highways. In this paper, we proposed a method and designed a learning-based detection model of lane-change behaviour in highway environment, which only needs the vehicle to be equipped with velocity and direction sensors or each section of the highway to have a video camera. First, based on the Next Generation Simulation (NGSIM) Interstate 80 Freeway Dataset, we analyzed the relevant features of lane-changing behaviour and preprocessed the data and then used machine learning algorithms to select the suitable features for lane-change detection. According to the result of feature selection, we chose the lateral velocity of the vehicle as the lane-change feature and used machine learning algorithms to learn the lane-change behaviour of the vehicle to detect it. From the dataset, continuous data of 14 vehicles with frequent lane changes were selected for experimental analysis. The experimental results show that the designed KNN lane-change detection model has the best performance with detection accuracy between 89.57% and 100% on the selected dataset, which can well complete the vehicle lane-change detection task.
车道变换行为的检测对于驾驶安全至关重要，尤其是在高速公路上。在本文中，我们提出了一种方法，并设计了一种基于学习的高速公路环境下车道变换行为检测模型，该模型只需要车辆配备速度和方向传感器，或者高速公路的每个路段都安装摄像头。首先，基于下一代仿真（NGSIM）80号州际公路数据集，我们分析了车道变换行为的相关特征并对数据进行预处理，然后使用机器学习算法选择适合车道变换检测的特征。根据特征选择的结果，我们选择车辆的横向速度作为车道变换特征，并使用机器学习算法学习车辆的车道变换行为以进行检测。从数据集中选取了14辆频繁变换车道车辆的连续数据进行实验分析。实验结果表明，所设计的KNN车道变换检测模型性能最佳，在所选数据集上的检测准确率在89.57%至100%之间，能够很好地完成车辆车道变换检测任务。
