学习合作估计路面摩擦
Learning to cooperatively estimate road surface friction
摘要：
我们提出了一个系统，用于估计在任何弯曲的路段的路面表面的摩擦，通过达成共识的估计，从最近通过该部分的车辆的数据的基础上。这个估计可以帮助跟踪车辆。为了降低成本，我们只依赖标准的汽车传感器，例如IMU，以及用于转向角度和车轮速度的传感器。我们系统的工作流程包括：（i）处理来自现有车辆传感器的测量结果，以实现捕获低摩擦对车辆的影响的虚拟传感器，（ii）使用V2X通信将短运动学概要从车辆传输到路边单元（RSU），以及（iii）通过在RSU处运行机器学习回归器来估计摩擦系数，从个别车辆的总结，然后结合几个这样的估计。在设计和实现我们的系统在道路网络上，我们面临两个关键问题：（i）每个单独的路段应该有一个本地的摩擦系数回归，或者我们可以使用一个全局回归，覆盖所有可能的路段？以及（ii）所得回归估计的准确性如何？我们测试我们的解决方案的设计变化的性能，使用模拟的商业软件包Dyna4。我们考虑一个单一的车辆类型与不同程度的轮胎磨损，和一系列的道路摩擦系数。我们发现：（a）与局部回归量相比，使用全局回归量仅会导致精度的边际损失，（b）如果组合基于至少50辆最近通过的车辆，则RSU处的共识估计具有约10%的最坏情况误差，以及（c）我们的回归量具有小于5%的均方根（RMS）误差。我们的系统的RMS差错率是商业摩擦估计服务的一半。
英文摘要：
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.
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