Environment perception is crucial for the development of autonomous driving and advanced driver assistance systems. The cooperative perception using the infrastructure sensors can significantly expand the field of view of on-board sensors and improve the accuracy of target tracking. In this paper, we propose a hybrid vehicular perception system that incorporates both received feature-level information from infrastructure sensors and track-level data from the multi-access edge computing server (MEC-Server). An infrastructure-enhanced multiple-model probability hypothesis density is proposed to handle the feature-level data from heterogeneous infrastructure sensors. The problem of kinematic state estimation is improved with the prior information of the road environment. Furthermore, a generic communication interface between the infrastructure sensor and MEC-Server is designed, which allows the object data to have the same notion of locality through the use of a generic object state model. Simulation results show that the presented algorithm provides higher accuracy and reliability after considering the prior information of the road environment.

中文翻译：

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使用多模型 PHD 滤波器的基础设施增强型多目标跟踪

环境感知对于自动驾驶和高级驾驶辅助系统的开发至关重要。使用基础设施传感器的协同感知可以显着扩大车载传感器的视野，提高目标跟踪的准确性。在本文中，我们提出了一种混合车辆感知系统，该系统结合了从基础设施传感器接收到的特征级信息和来自多访问边缘计算服务器（MEC-Server）的轨道级数据。提出了一种基础设施增强的多模型概率假设密度来处理来自异构基础设施传感器的特征级数据。利用道路环境的先验信息改进了运动学状态估计问题。此外，设计了基础设施传感器和MEC-Server之间的通用通信接口，通过使用通用对象状态模型，允许对象数据具有相同的局部性概念。仿真结果表明，在考虑了道路环境的先验信息后，所提算法具有更高的准确性和可靠性。