The technology of Artificial Intelligence (AI) brings tremendous possibilities for autonomous vehicle applications. One of the essential tasks of autonomous vehicle is environment perception using machine learning algorithms. Since the cyclists are the vulnerable road users, cyclist detection and tracking are important perception sub-tasks for autonomous vehicles to avoid vehicle-cyclist collision. In this paper, a robust method for cyclist detection and tracking is presented based on multi-layer laser scanner, i.e., IBEO LUX 4L, which obtains four-layer point cloud from local environment. First, the laser points are partitioned into individual clusters using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) method based on subarea. Then, 37-dimensional feature set is optimized by Relief algorithm and Principal Component Analysis (PCA) to produce two new feature sets. Support Vector Machine (SVM) and Decision Tree (DT) classifiers are further combined with three feature sets, respectively. Moreover, Multiple Hypothesis Tracking (MHT) algorithm and Kalman filter based on Current Statistical (CS) model are applied to track moving cyclists and estimate the motion state. The performance of the proposed cyclist detection and tracking method is validated in real road environment.

中文翻译：

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基于多层激光扫描仪的自行车骑行检测与追踪

人工智能（AI）技术为自动驾驶汽车应用带来了巨大的可能性。自动驾驶汽车的基本任务之一是使用机器学习算法进行环境感知。由于骑自行车者是易受伤害的道路使用者，因此骑自行车者的检测和跟踪是自动驾驶汽车避免骑自行车者碰撞的重要感知子任务。本文提出了一种基于多层激光扫描仪IBEO LUX 4L的强健的单车检测和跟踪方法，该方法可以从局部环境中获取四层点云。首先，基于分区的基于噪声的应用程序基于密度的空间聚类（DBSCAN）方法将激光点划分为各个簇。然后，通过救济算法和主成分分析（PCA）对37维特征集进行了优化，以生成两个新的特征集。支持向量机（SVM）和决策树（DT）分类器分别进一步与三个功能集组合。此外，基于当前统计（CS）模型的多重假设跟踪（MHT）算法和卡尔曼滤波器被应用于跟踪运动的自行车手并估计运动状态。在实际道路环境中验证了所提出的骑自行车者检测和跟踪方法的性能。运用多重假设跟踪（MHT）算法和基于当前统计（CS）模型的卡尔曼滤波器来跟踪运动的自行车骑手并估计运动状态。在实际道路环境中验证了所提出的骑自行车者检测和跟踪方法的性能。运用多重假设跟踪（MHT）算法和基于当前统计（CS）模型的卡尔曼滤波器来跟踪运动的自行车骑手并估计运动状态。在实际道路环境中验证了所提出的骑自行车者检测和跟踪方法的性能。