基于机器学习的单线激光雷达进行车辆识别与跟踪方法研究
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摘要
针对多线激光雷达应用于无人驾驶车辆的高额造价问题,提出了一种基于单线激光雷达作为主传感器,并利用机器学习方法实现车辆识别与跟踪任务。首先通过激光雷达扫描并获取空间轮廓数据,对每帧数据采用层次聚类算法进行目标分割;然后对每个目标进行特征提取,通过交叉验证和网格搜索对支持向量机的参数进行优化以实现更好的分类效果,采用卡尔曼滤波实现目标车辆的跟踪;最后在城市公路和高架桥上开展了数据采集的工作,使用了装备单线激光雷达和相机的乘用车作为实验平台,实验结果表明,所提取六种目标车辆特征值组成特征向量并配合参数优化后的支持向量机可以实现较高识别率,并可实现对目标车辆的稳定跟踪。
Considering the high cost of multi-layer LIDAR application for unmanned vehicles,in this paper,a vehicle detection and tracking method based on single-layer LIDAR using machine learning techniques is proposed. Firstly,the spatial profile data is acquired by LIDAR scaning. And then hierarchical clustering algorithm is applied to divide the target data for each frame. Furthermore,the features are extracted and the optimized parameters of Support Vector Machine(SVM) are found through cross validation and grid search to achieve better classification results. The Kalman filter is used to track the target vehicle. Finally,a passenger vehicle OptoBot-IV which is equipped with a single-layer LIDAR and a camera is driven on the urban area for the data collection. The experimental results show that this approach can achieve a reasonable high recognition accuracy and achieve stable tracking of the object vehicles.
Considering the high cost of multi-layer LIDAR application for unmanned vehicles,in this paper,a vehicle detection and tracking method based on single-layer LIDAR using machine learning techniques is proposed. Firstly,the spatial profile data is acquired by LIDAR scaning. And then hierarchical clustering algorithm is applied to divide the target data for each frame. Furthermore,the features are extracted and the optimized parameters of Support Vector Machine(SVM) are found through cross validation and grid search to achieve better classification results. The Kalman filter is used to track the target vehicle. Finally,a passenger vehicle OptoBot-IV which is equipped with a single-layer LIDAR and a camera is driven on the urban area for the data collection. The experimental results show that this approach can achieve a reasonable high recognition accuracy and achieve stable tracking of the object vehicles.
引文
[1]王世峰,戴祥,徐宁,等.无人驾驶汽车环境感知技术综述[J].长春理工大学学报:自然科学版,2017,40(1):1-6.
[2] Wender S,Dietmayer K. 3D vehicle detection using a laser scanner and a video camera[J].IET Intelligent Transport Systems.Istanbul,2008,2(2):105-112.
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[5]钟林辉,张能伟,侯长源,等.一种改进的基于层次聚类的软件重构技术研究[J].计算机工程与应用,2015,51(20):50-54.
[6]庆鹏程,陈昆山.基于点云处理技术的汽车车身尺寸获取研究[J].科学技术与工程,2014,14(23):261-264.
[7]满增光,叶文华,肖海宁,等.从激光扫描数据中提取角点特征的方法[J].南京航空航天大学学报,2012,44(3):379-383.
[8] Sebald D J,Bucklew J A. Support vector machines and the multiple hypothesis test problem[J]. IEEE Traps on Signal processing,2001,49(11):2865-2872.
[9]张华.DDAG支持向量机在ERT系统流型识别中的应用[J].长春理工大学学报:自然科学版,2015,38(4):159-162.
[10] Annabella A,Antonio F. The Proximal Trajectory Algorithm in SVM Cross Validation[J]. IEEE Transactions on Neural Networks and Learning Systems,2016,27(5):966-977.
[11] Anderson J L. An ensemble adjustment Kalman filter for data assimilation[J]. Monthly Weather Review,2001,129(12):2884-2903.
[2] Wender S,Dietmayer K. 3D vehicle detection using a laser scanner and a video camera[J].IET Intelligent Transport Systems.Istanbul,2008,2(2):105-112.
[3]高韬,刘正光,张军,等.交通车辆轮廓跟踪算法研究及其应用[J].应用基础与工程学学报,2010,18(2):343-351.
[4]高磊,李超,朱成军,等.基于边缘对称性的视频车辆检测算法[J].北京航空航天大学学报,2008,34(9):1113-1116.
[5]钟林辉,张能伟,侯长源,等.一种改进的基于层次聚类的软件重构技术研究[J].计算机工程与应用,2015,51(20):50-54.
[6]庆鹏程,陈昆山.基于点云处理技术的汽车车身尺寸获取研究[J].科学技术与工程,2014,14(23):261-264.
[7]满增光,叶文华,肖海宁,等.从激光扫描数据中提取角点特征的方法[J].南京航空航天大学学报,2012,44(3):379-383.
[8] Sebald D J,Bucklew J A. Support vector machines and the multiple hypothesis test problem[J]. IEEE Traps on Signal processing,2001,49(11):2865-2872.
[9]张华.DDAG支持向量机在ERT系统流型识别中的应用[J].长春理工大学学报:自然科学版,2015,38(4):159-162.
[10] Annabella A,Antonio F. The Proximal Trajectory Algorithm in SVM Cross Validation[J]. IEEE Transactions on Neural Networks and Learning Systems,2016,27(5):966-977.
[11] Anderson J L. An ensemble adjustment Kalman filter for data assimilation[J]. Monthly Weather Review,2001,129(12):2884-2903.