标题： 汽车速度估计：从OBD-II到ADAS的传感器类型与误差特性 显示英文标题

标题： Automotive Speed Estimation: Sensor Types and Error Characteristics from OBD-II to ADAS

摘要： 现代车载导航系统严重依赖于将速度测量与惯性导航系统（INS）和全球导航卫星系统（GNSS）相结合。基于遥测的应用程序通常从车载诊断系统II（OBD-II）获取速度数据。然而，不同车辆的速度推导方法以及用于测量车轮速度的传感器类型各不相同。这些差异导致了必须在导航和自主应用中考虑的不同误差特性。本文通过研究标准汽车系统中使用的各种速度传感技术以及高级系统中用于更高自主级别的替代技术（如高级驾驶辅助系统（ADAS）、自动驾驶（AD）或测绘应用）来填补这一空白。我们提出了一种识别车辆速度传感器类型的方法，并提出了准确建模其误差特性的策略。为了验证我们的方法，我们在加拿大安大略省的多伦多和金斯顿的城市环境中收集并分析了三条长时间的真实道路轨迹的数据。结果强调了整合多种传感器模式以实现更准确的速度估计的重要性，从而改善了汽车导航状态估计，特别是在GNSS受限的环境中。 显示英文摘要

摘要： Modern on-road navigation systems heavily depend on integrating speed measurements with inertial navigation systems (INS) and global navigation satellite systems (GNSS). Telemetry-based applications typically source speed data from the On-Board Diagnostic II (OBD-II) system. However, the method of deriving speed, as well as the types of sensors used to measure wheel speed, differs across vehicles. These differences result in varying error characteristics that must be accounted for in navigation and autonomy applications. This paper addresses this gap by examining the diverse speed-sensing technologies employed in standard automotive systems and alternative techniques used in advanced systems designed for higher levels of autonomy, such as Advanced Driver Assistance Systems (ADAS), Autonomous Driving (AD), or surveying applications. We propose a method to identify the type of speed sensor in a vehicle and present strategies for accurately modeling its error characteristics. To validate our approach, we collected and analyzed data from three long real road trajectories conducted in urban environments in Toronto and Kingston, Ontario, Canada. The results underscore the critical role of integrating multiple sensor modalities to achieve more accurate speed estimation, thus improving automotive navigation state estimation, particularly in GNSS-denied environments.