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首页> 外文期刊>Intelligent Transportation Systems, IEEE Transactions on >Object Matching for Inter-Vehicle Communication Systems—An IMM-Based Track Association Approach With Sequential Multiple Hypothesis Test
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Object Matching for Inter-Vehicle Communication Systems—An IMM-Based Track Association Approach With Sequential Multiple Hypothesis Test

机译：车辆间通信系统的对象匹配—基于IMM的航迹关联方法和顺序多重假设检验

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Autonomous driving poses unique challenges for vehicle environment perception due to the complex driving environment where the autonomous vehicle interacts with surrounding traffic participants. Due to the limited capability of any sensor perception system, it is highly desirable that an autonomous driving vehicle could use not only information from onboard sensors (say, radar/camera/lidar) but also from remote (network) information via inter-vehicle communication systems. The collaborative information from cooperativeon-cooperative remote vehicles (along with the onboard sensor data) could substantially improve the vehicle decision making process and push autonomous driving to be safer and more reliable. Inter-vehicle communication technologies are at the stage of development for market introduction, after years of research and standardization. In this paper, we setup a dedicated short range communication (DSRC) system to provide a low-latency inter-vehicle wireless communication channel. The task is to build a record linkage between the onboard sensor data and the corresponding DSRC-transmitted remote vehicle information when both sets belong to the same object, for the purpose of enhancing host vehicle environment perceiving capability and reliability. This is a typical data association problem. The challenges mainly lie in the inherent uncertain nature of the observation data and the practical issues that information often suffers from delays and drops. We propose a track-based association approach using an interacting multiple model estimator with a sequential multiple hypothesis test (denoted as IMM-SMHT) as an ubiquitous solution to handle different situations in complicated driving scenarios. To fully exploit the potential of such a system, only position information (from the DSRC channel and onboard radar system) is used for the object matching purpose-we try to use the least amount of information to achieve a high association accuracy; additional information can be used but not currently considered. We aim to provide a real world solution, and therefore, a prototype vehicle system is built with practical consideration on market availability, cost, and sensor limitations. We design meaningful use cases for creating functionality modules from a systematic point of view. The inter-vehicle information fusion system based on the track fusion approach using the IMM-SMHT is tested in real traffic on the U.S. roads and shows promising object matching performance of significant practical feasibility.
机译：由于自动驾驶车辆与周围交通参与者互动的复杂驾驶环境，自动驾驶对车辆环境提出了独特的挑战。由于任何传感器感知系统的能力有限，因此非常希望自动驾驶车辆不仅可以使用车载传感器（例如，雷达/摄像头/雷达）中的信息，还可以通过车内通信使用远程（网络）信息系统。来自合作/非合作远程车辆的协作信息（以及车载传感器数据）可以大大改善车辆决策过程，并推动自动驾驶更加安全和可靠。经过多年的研究和标准化，车辆间通信技术正处于市场引入的发展阶段。在本文中，我们建立了专用的短距离通信（DSRC）系统，以提供低延迟的车间间无线通信通道。任务是在两组传感器都属于同一对象时，在车载传感器数据和相应的DSRC传输的远程车辆信息之间建立记录链接，以增强宿主车辆环境感知能力和可靠性。这是一个典型的数据关联问题。挑战主要在于观测数据固有的不确定性以及信息经常遭受延误和掉落的实际问题。我们提出了一种基于轨迹的关联方法，该方法使用交互的多模型估计量与顺序多重假设检验（称为IMM-SMHT）作为解决复杂驾驶场景中不同情况的普遍解决方案。为了充分利用这种系统的潜力，仅将位置信息（来自DSRC通道和车载雷达系统）用于对象匹配目的-我们尝试使用最少的信息量来实现较高的关联精度；可以使用其他信息，但目前尚未考虑。我们旨在提供一个现实的解决方案，因此，在构建原型车辆系统时会考虑市场可用性，成本和传感器限制。我们从系统的角度设计有意义的用例，以创建功能模块。在美国道路上的实际交通中对基于使用IMM-SMHT的轨道融合方法的车辆间信息融合系统进行了测试，并显示了具有重大实际可行性的有希望的对象匹配性能。

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