Advanced driver assistance systems and highly automated driving functions require an enhanced frontal perception system. The requirements of a frontal environment perception system cannot be satisfied by either of the existing automotive sensors. A commonly used sensor cluster for these functions consists of a mono-vision smart camera and automotive radar. The sensor fusion is intended to combine the data of these sensors to perform a robust environment perception. Multi-object tracking algorithms have a suitable software architecture for sensor data fusion. Several multi-object tracking algorithms, such as JPDAF or MHT, have good tracking performance; however, the computational requirements of these algorithms are significant according to their combinatorial complexity. The GM-PHD filter is a straightforward algorithm with favorable runtime characteristics that can track an unknown and time-varying number of objects. However, the conventional GM-PHD filter has a poor performance in object cardinality estimation. This paper proposes a method that extends the GM-PHD filter with an object birth model that relies on the sensor detections and a robust object extraction module, including Bayesian estimation of objects’ existence probability to compensate for drawbacks of the conventional algorithm.

中文翻译：

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用于汽车正面感知系统的基于 GM-PHD 滤波器的传感器数据融合


先进的驾驶员辅助系统和高度自动化的驾驶功能需要增强的正面感知系统。现有的任何一种汽车传感器都无法满足正面环境感知系统的要求。用于这些功能的常用传感器集群由单视觉智能摄像头和汽车雷达组成。传感器融合旨在结合这些传感器的数据以执行强大的环境感知。多目标跟踪算法具有适合传感器数据融合的软件架构。多种多目标跟踪算法，如JPDAF或MHT，具有良好的跟踪性能；然而，由于这些算法的组合复杂性，其计算要求非常高。 GM-PHD 过滤器是一种简单的算法，具有良好的运行时特性，可以跟踪未知且随时间变化的数量的对象。然而，传统的GM-PHD滤波器在对象基数估计方面的性能较差。本文提出了一种利用对象诞生模型扩展 GM-PHD 滤波器的方法，该模型依赖于传感器检测和鲁棒的对象提取模块，包括对象存在概率的贝叶斯估计，以弥补传统算法的缺点。