Human drivers take instant decisions about their speed, acceleration and distance from other vehicles based on different factors including their estimate of the road roughness. Having an accurate algorithm for real-time evaluation of road roughness can be critical for autonomous vehicles in order to achieve safe driving and passengers comfort. In this paper, we investigate the problem of interactive road roughness identification. We propose a novel inverse algorithm based on the knowledge of a vehicle dynamic characteristics and dynamic responses. The algorithm construct the road profile in time using one-iteration to update the wheels forces which are then used to identify the road roughness. The relation between the forces and the road profile is defined by a system of ordinary differential equations that are solved using the composite Gaussian quadrature. To reduce the error accumulation in time when noisy data is used for the vehicle response, a bidirectional filter is also implemented. We assume a simple model that is based on four degrees-of-freedom system and vibration acceleration measurements to evaluate the road roughness in real time. Although we present the results for this specific model, the algorithm can also be utilised with models of any number of degrees of freedom and can deal with models where the dynamic response is only available at some of the degrees of freedom. This is achieved by introducing a matrix reduction technique that is discussed in details. Furthermore, we evaluate the impact of uncertainty in the vehicle parameters on the algorithm estimation accuracy. The proposed algorithm is evaluated for different types of road roughness. The simulation results show that the proposed method is robust and can achieve high accuracy. The algorithm offers excellent potential for road roughness estimation not only for autonomous vehicle but also for vehicles and roads designing purposes.

中文翻译：

---

自动驾驶车辆实时道路粗糙度估计的逆算法

驾驶员会根据不同的因素，包括对道路不平度的估计，立即做出有关其速度，加速度和与其他车辆的距离的决策。为了实现安全驾驶和乘客舒适度，拥有用于实时评估道路粗糙度的精确算法对于自动驾驶汽车至关重要。在本文中，我们研究了交互式道路粗糙度识别的问题。我们基于车辆动态特性和动态响应的知识提出了一种新颖的逆算法。该算法使用一次迭代来及时构造道路轮廓，以更新车轮力，然后将车轮力用于识别道路粗糙度。力与道路轮廓之间的关系由使用复合高斯求积法求解的常微分方程组定义。为了减少在将噪声数据用于车辆响应时的时间误差累积，还实现了双向滤波器。我们假设一个简单的模型基于4个自由度系统和振动加速度测量值来实时评估道路粗糙度。尽管我们给出了该特定模型的结果，但是该算法也可以用于任意数量的自由度模型，并且可以处理仅在某些自由度下可获得动态响应的模型。这是通过引入详细讨论的矩阵约简技术来实现的。此外，我们评估车辆参数的不确定性对算法估计精度的影响。针对不同类型的道路不平度对提出的算法进行了评估。仿真结果表明，该方法是鲁棒的，可以达到较高的精度。该算法不仅为自动驾驶汽车而且为车辆和道路设计目的提供了用于道路粗糙度估计的极佳潜力。