基于车轮传感器数据的异常路面检测
Abnormal Road Surface Detection Using Wheel Sensor Data
摘要：
智能轮胎可用于从轮胎压力监测到分析轮胎/道路相互作用、车轮负载和胎面磨损监测的广泛应用。在这篇文章中，我们开发了一个测试系统，智能轮胎配备了一个三维压阻式力传感器。传感器的输出被分割成轮胎旋转周期，然后由依赖于自适应Hermite函数的变换表示。这一步骤背后的基本思想是提取捕获轮胎动态的相关特征。然后，我们评估所提出的测量系统在一个潜在的车辆应用，即异常路面检测。我们通过开发低复杂度的分析和数据驱动的机器学习算法来处理相应的二进制分类问题，这些算法在真实世界的测量数据上进行了测试。我们的实验表明，所提出的方法是能够检测异常的路面上的平均准确率超过97%。
英文摘要：
Intelligent tires can be used for a wide array of applications ranging from tire pressure monitoring to analyzing tire/road interactions, wheel loading, and tread wear monitoring. In this article, we develop a measurement system for intelligent tires equipped with a 3-D piezoresistive force sensor. The output of the sensor is segmented into tire revolution cycles, which are then represented by a transformation relying on adaptive Hermite functions. The underlying idea behind this step is to extract relevant features which capture tire dynamics. Then we evaluate the proposed measurement system in a potential vehicle application, that is, abnormal road surface detection. We deal with the corresponding binary classification problem by developing both low-complexity analytical and data-driven machine learning algorithms, which are tested on real-world measurement data. Our experiments showed that the proposed methods are able to detect abnormalities on the road surface with a mean accuracy of over 97%.
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。
