This study constructs a nonlinear dynamic model of articulated vehicles and a model of hydraulic steering system. The equations of state required for nonlinear vehicle dynamics models, stability analysis models, and corresponding eigenvalue analysis are obtained by constructing Newtonian mechanical equilibrium equations. The objective and subjective causes of the snake oscillation and relevant indicators for evaluating snake instability are analysed using several vehicle state parameters. The influencing factors of vehicle stability and specific action mechanism of the corresponding factors are analysed by combining the eigenvalue method with multiple vehicle state parameters. The centre of mass position and hydraulic system have a more substantial influence on the stability of vehicles than the other parameters. Vehicles can be in a complex state of snaking and deviating. Different eigenvalues have varying effects on different forms of instability. The critical velocity of the linear stability analysis model obtained through the eigenvalue method is relatively lower than the critical velocity of the nonlinear model.

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This study examines the roll instability mechanism and stability index of articulated steering vehicles (ASVs) by taking wheel loaders as the research object. A seven-degree-of-freedom nonlinear dynamics model of the ASVs is built on the basis of multibody dynamics. A physical prototype model of an ASV is designed and manufactured to validate the dynamic model. Test results reasonably agree with the simulation results, which indicates that the established dynamic model can reasonably describe ASV movements. Detailed analysis of the rollover stability of the wheel loader is performed with the use of the established dynamic model. Analysis results show that rollover will occur when the roll angular velocity exceeds a critical threshold, which is affected by lateral acceleration and slope angle. On this basis, a dynamic stability index applicable to the ASVs is presented.

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Bucket fill factor is of paramount importance in measuring the productivity of construction vehicles, which is the percentage of materials loaded in the bucket within one scooping. Additionally, the locational information of the bucket is also indispensable for scooping trajectory planning. Some research has been conducted to measure it via state-of-the-art computer vision approaches, but their robustness against various environment conditions is not considered. The aim of this study is to fill this gap and six distinctive environment settings are included. Images captured by a stereo camera are used to generate point clouds before being structured into 3D maps. This novel preprocessing pipeline for deep learning is originally proposed and its feasibility has been validated through this study. Moreover, multitask learning is employed to exploit the positive relationship among two tasks: fill factor prediction and bucket detection. Therefore, after preprocessing, 3D maps are forwarded to a faster region with convolutional neural network incorporated with an improved residual neural network. The value of fill factor is acquired via a classification and probabilistic-based approach, which is novel, achieving an inspiring result (overall volume estimation accuracy: 95.23% and detection precision: 92.62%) at the same time.