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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. [ABSTRACT FROM AUTHOR]

Using a 3-degree-of-freedom lateral dynamic vehicle-trailer system model with the LuGre tyre model, this paper proposes a constrained unscented transformation based scheme for estimating the lateral velocity of a vehicle trailer. It is shown that the proposed lateral velocity estimation scheme is robust to the road condition. An affine quadratic stability approach is used to analyze the stability of the proposed estimation scheme. Simulations studies in CarSim/Simulink along with experimental tests are used to validate the proposed scheme. The test results confirm the accuracy of the developed estimation scheme and convergence of the vehicle-trailer lateral velocity estimate to the actual value. [ABSTRACT FROM AUTHOR]