We present a system for estimating the friction of the pavement surface at any curved road section, by arriving at a consensus estimate, based on data from vehicles that have recently passed through that section. This estimate can help following vehicles. To keep costs down, we depend only on standard automotive sensors, such as the IMU, and sensors for the steering angle and wheel speeds. Our system's workflow consists of: (i) processing of measurements from existing vehicular sensors, to implement a virtual sensor that captures the effect of low friction on the vehicle, (ii) transmitting short kinematic summaries from vehicles to a road side unit (RSU), using V2X communication, and (iii) estimating the friction coefficients, by running a machine learning regressor at the RSU, on summaries from individual vehicles, and then combining several such estimates. In designing and implementing our system over a road network, we face two key questions: (i) should each individual road section have a local friction coefficient regressor, or can we use a global regressor that covers all the possible road sections? and (ii) how accurate are the resulting regressor estimates? We test the performance of design variations of our solution, using simulations on the commercial package Dyna4. We consider a single vehicle type with varying levels of tyre wear, and a range of road friction coefficients. We find that: (a) only a marginal loss of accuracy is incurred in using a global regressor as compared to local regressors, (b) the consensus estimate at the RSU has a worst case error of about ten percent, if the combination is based on at least fifty recently passed vehicles, and (c) our regressors have root mean square (RMS) errors that are less than five percent. The RMS error rate of our system is half as that of a commercial friction estimation service.

翻译：我们提出一种系统，用于估计任意弯曲路段的路面摩擦系数。该系统通过整合近期经过该路段的车辆数据达成共识估计，从而为后续车辆提供参考。为降低成本，我们仅依赖标准车载传感器（如惯性测量单元、转向角传感器和轮速传感器）。系统工作流程包括：（i）处理现有车辆传感器的测量数据，实现虚拟传感器以捕捉低摩擦对车辆的影响；（ii）通过V2X通信将车辆运动学摘要传输至路侧单元；（iii）在路侧单元上运行机器学习回归器，基于单辆车的摘要数据估计摩擦系数，随后融合多个估计结果。在路网中设计与部署该系统时，需解决两个关键问题：（i）每个路段应使用局部摩擦系数回归器，还是可采用覆盖所有路段的全局回归器？（ii）回归器估计的精度如何？我们利用商用仿真软件Dyna4测试了不同设计方案性能，考虑单车型搭配不同轮胎磨损程度及多种路面摩擦系数。实验发现：（a）与局部回归器相比，全局回归器精度损失极小；（b）当融合至少50辆近期通过车辆的数据时，路侧单元共识估计的最大误差约为10%；（c）回归器的均方根误差低于5%。本系统的均方根误差率仅为商用摩擦估计服务的一半。