汽车速度估计：从OBD-II到ADAS的传感器类型与误差特性
Automotive Speed Estimation: Sensor Types and Error Characteristics from OBD-II to ADAS
AI总结：
本文研究汽车速度估计中传感器类型与误差特性，提出识别速度传感器类型及建模误差的方法，并通过城市道路数据验证，强调多传感器融合对提升导航精度的重要性。
摘要：
现代车载导航系统高度依赖将速度测量与惯性导航系统（INS）和全球导航卫星系统（GNSS）进行集成。基于遥测的应用通常从车载诊断系统II（OBD-II）获取速度数据。然而，不同车辆在速度推导方法以及用于测量车轮速度的传感器类型上存在差异。这些差异导致不同的误差特性，必须在导航和自动驾驶应用中加以考虑。本文通过研究标准汽车系统中使用的多样化速度传感技术，以及为更高水平自动驾驶（如高级驾驶辅助系统（ADAS）、自动驾驶（AD）或测绘应用）设计的先进系统中使用的替代技术，填补了这一空白。我们提出了一种识别车辆中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。为验证我们的方法，我们收集并分析了在加拿大安大略省多伦多和金斯敦的城市环境中进行的三个长距离真实道路轨迹数据。结果强调了集成多种传感器模态以实现更准确速度估计的关键作用，从而改善汽车导航状态估计，尤其是在GNSS信号缺失的环境中。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
