This paper proposes two different approaches for estimating grade and bank angles for arbitrary vehicle-trailer configurations independent from road friction conditions: model-based and Machine Learning (ML) approaches. The model-based method employs unknown input observers on a vehicle-trailer roll/pitch dynamic model with fault thresholds. In the proposed ML approach, a Recurrent Neural Network (RNN) with long-short term memory gates is designed to estimate the road angles. The inputs of the RNN have been selected based on the vehicle-trailer roll and pitch dynamic models, and are normalised by the vehicle wheel-base, mass, and centre of gravity height so that the network is modularly applicable to different trailer types. The simulation and experimental test results justify the performance of the proposed road-bank and grade-angle estimation scheme in various cases and demonstrate that both bank and grade angles can be estimated with high accuracy.

本文提出了两种不同的方法来估计任意车辆拖车配置的坡度和坡度角，独立于道路摩擦条件：基于模型的方法和机器学习 (ML) 方法。基于模型的方法在具有故障阈值的车辆拖车侧倾/俯仰动态模型上采用未知输入观测器。在提出的 ML 方法中，设计了具有长短期记忆门的循环神经网络 (RNN) 来估计道路角度。RNN 的输入是基于车辆-挂车侧倾和俯仰动态模型选择的，并通过车辆轴距、质量和重心高度进行归一化，以便网络模块化适用于不同的挂车类型。