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首页> 外文期刊>IEEE Transactions on Vehicular Technology >Multiple Vehicle Tracking Based on Labeled Multiple Bernoulli Filter Using Pre-Clustered Laser Range Finder Data
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Multiple Vehicle Tracking Based on Labeled Multiple Bernoulli Filter Using Pre-Clustered Laser Range Finder Data

机译：基于聚类激光测距仪的带标记多伯努利滤波器的多车跟踪

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摘要

Multiple vehicle tracking (MVT) system is a prerequisite to path planning and decision making of self-driving cars as it can provide positions of surrounding vehicles. Most of the available approaches belonging to the so called tracking-by-detection approach inevitably bring detection errors into the tracking result. In this study, we proposed a laser range finder (LRF) based track-before-detect MVT algorithm without detection procedure. Moreover, different from the state of the art in track-before-detect approaches using raw data, we applied a pre-clustering procedure to segment the raw data into disjoint clusters to reduce computation demand. Specifically, a clustering algorithm named iterative nearest point search (INPS) which can even handle the partial occlusion situations that are challenging for traditional clustering algorithms was designed for the pre-clustering procedure. Furthermore, a detailed cluster-to-target measurement model was proposed to describe the difference between cluster and hypothesis vehicle. Finally, we integrated the measurement model into the labeled multi-Bernoulli filter with particle implementation. Simulations and experiments show that the proposed MVT algorithm provides more accurate estimates of vehicle number and position in comparison with conventional methods.
机译：多车辆跟踪（MVT）系统是自动驾驶汽车的路径规划和决策的前提，因为它可以提供周围车辆的位置。属于所谓的逐次检测跟踪方法的大多数可用方法不可避免地将检测错误带入跟踪结果。在这项研究中，我们提出了一种基于激光测距仪（LRF）的无检测前跟踪MVT算法。此外，与使用原始数据进行先行检测的方法不同，我们应用了预聚类过程将原始数据分割为不相交的簇以减少计算需求。具体而言，针对预聚类过程设计了一种名为迭代最近点搜索（INPS）的聚类算法，该算法甚至可以处理传统聚类算法所面临的部分遮挡情况。此外，提出了详细的聚类到目标测量模型来描述聚类和假设媒介之间的差异。最后，我们将测量模型集成到带有粒子实现的标记多伯努利滤波器中。仿真和实验表明，与常规方法相比，所提出的MVT算法提供了更准确的车辆数量和位置估计。

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