Intersection-Based Road User Tracking Using a Classifying Multiple-Model PHD Filter
Intersection-Based Road User Tracking Using a Classifying Multiple-Model PHD Filter
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DOI：
10.1109/mits.2014.2304754
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发表时间：
2014-04
影响因子：
3.6
通讯作者：
D. Meissner;Stephan Reuter;Elias Strigel;K. Dietmayer
中科院分区：
文献类型：
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作者：
D. Meissner;Stephan Reuter;Elias Strigel;K. Dietmayer
城市十字路口涉及行人和骑自行车者的致命事故数量仍在增加。因此，基于交叉口的感知系统为车辆提供交叉口场景的动态模型。在此基础上，交叉口感知有助于区分遮挡，预计将显着减少交叉口的事故数量。因此，本文提出了一种通用的多传感器跟踪算法，即分类多模型概率假设密度（CMMPHD）滤波器，它有助于使用单个滤波器对相关对象进行跟踪和分类。由于运动特性不同，需要采用多模型方法来获得所有类型物体的准确状态估计和持久轨迹。此外，还提出了 PHD 滤波器的扩展，以处理基于 Dempster-Shafer 证据理论的不同传感器类型的矛盾测量。使用公共十字路口的真实传感器数据来评估跟踪和分类的性能。
The number of fatal accidents involving pedestrians and bikers at urban intersections is still increasing. Therefore, an intersection-based perception system provides a dynamic model of the intersection scene to the vehicles. Based on that, the intersection perception facilitates to discriminate occlusions which is expected to significantly reduce the number of accidents at intersections. Therefore this contribution presents a general purpose multi-sensor tracking algorithm, the classifying multiple-model probability hypothesis density (CMMPHD) filter, which facilitates the tracking and classification of relevant objects using a single filter. Due to the different motion characteristics, a multiple-model approach is required to obtain accurate state estimates and persistent tracks for all types of objects. Additionally, an extension of the PHD filter to handle contradictory measurements of different sensor types based on the Dempster-Shafer theory of evidence is proposed. The performance of tracking and classification is evaluated using real world sensor data of a public intersection.