标题： 通过融合标准车载汽车传感器进行道路粗糙度估计 显示英文标题

标题： Road Roughness Estimation via Fusion of Standard Onboard Automotive Sensors

摘要： 道路不平度显著影响车辆振动和乘坐质量。 我们引入了一种基于卡尔曼滤波器（KF）的方法，通过融合惯性和速度测量数据，以国际不平度指数（IRI）来估计道路不平度，为路面监测提供了一种成本效益高的解决方案。 该方法涉及对物理车辆进行系统识别，以估计现实的模型参数，随后通过基于KF的纵向道路剖面重建来计算IRI值。 它探讨了利用垂直和横向振动进行IRI估计，后者在现代车辆中更为常见。 在230公里真实数据上的验证显示了有前景的结果，IRI估计误差范围为参考值的1%至10%。 然而，当仅使用横向振动时，准确性显著下降，突显了其局限性。 这些发现证明了基于KF的估计在高效道路不平度监测方面的潜力。 显示英文摘要

摘要： Road roughness significantly affects vehicle vibrations and ride quality. We introduce a Kalman filter (KF)-based method for estimating road roughness in terms of the international roughness index (IRI) by fusing inertial and speed measurements, offering a cost-effective solution for pavement monitoring. The method involves system identification on a physical vehicle to estimate realistic model parameters, followed by KF-based reconstruction of the longitudinal road profile to compute IRI values. It explores IRI estimation using vertical and lateral vibrations, the latter more common in modern vehicles. Validation on 230 km of real-world data shows promising results, with IRI estimation errors ranging from 1% to 10% of the reference values. However, accuracy deteriorates significantly when using only lateral vibrations, highlighting their limitations. These findings demonstrate the potential of KF-based estimation for efficient road roughness monitoring.