It is very necessary for an intelligent heavy truck to have the ability to prevent rollover independently. However, it was rarely considered in intelligent vehicle motion planning. To improve rollover stability, a motion planning strategy with autonomous anti rollover ability for an intelligent heavy truck is put forward in this paper. Considering the influence of unsprung mass in the front axle and the rear axle and the body roll stiffness on vehicle rollover stability, a rollover dynamics model is built for the intelligent heavy truck. From the model, a novel rollover index is derived to evaluate vehicle rollover risk accurately, and a model predictive control algorithm is applicated to design the motion planning strategy for the intelligent heavy truck, which integrates the vehicle rollover stability, the artificial potential field for the obstacle avoidance, the path tracking and vehicle dynamics constrains. Then, the optimal path is obtained to meet the requirements that the intelligent heavy truck can avoid obstacles and drive stably without rollover. In addition, three typical scenarios are designed to numerically simulate the dynamic performance of the intelligent heavy truck. The results show that the proposed motion planning strategy can avoid collisions and improve vehicle rollover stability effectively even under the worst driving scenarios.

The paper makes a study on the relation between power consumption decline of air compressor and oil saving of heavy-duty vehicle. First, on the basis of theoretical mathematical equations and actual bench test, the paper clearly analyzes the effective and ineffective shaft powers of air compressor. Second, road test is carried out so as to describe the relation between the load rate of air compressor and the average speed of vehicle. Finally, based on the research outcomes, the paper puts forward an effective approach of reducing power consumption of air compressor which synchronously improves the oil saving of heavy-duty vehicle.

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Anti-rollover is an important performance for automated heavy trucks, which has been seldomly considered in the motion planning. This paper proposes an anti-rollover motion planning based on model predictive control (MPC) for automated heavy trucks. Taking the coupling of roll motion of sprung mass of the front axle with that of the drive axle into consideration, a seven degrees of freedom rollover dynamics model is established, and an evaluation index that can accurately describe the rollover motion is derived for heavy trucks. Then, a model predictive control strategy is designed for motion planning that combines the rollover dynamics, the artificial potential field for obstacle avoidance, and the trajectory tracking. In addition, the optimal path is calculated that considers collision avoidance, anti-rollover and vehicle dynamic constraints. Furthermore, three typical scenarios are applied to validate the performance of the proposed motion planning algorithm. The obtained results demonstrate that the proposed anti-rollover motion planning can effectively avoid collisions and reduce the rollover risk simultaneously when confronting edge scenarios.

Compactly supported orthogonal wavelet filters are extensively applied to the analysis and description of abrupt signals in fields such as multimedia. Based on the application of an elementary method for compactly supported orthogonal wavelet filters and the construction of a system of nonlinear equations for filter coefficients, we design compactly supported orthogonal wavelet filters, in which both the scaling and wavelet functions have many vanishing moments, by approximately solving the system of nonlinear equations. However, when solving such a system about filter coefficients of compactly supported wavelets, the most widely used method, the Newton Iteration method, cannot converge to the solution if the selected initial value is not near the exact solution. For such, we propose optimization algorithms for the Gauss-Newton type method that expand the selection range of initial values. The proposed method is optimal and promising when compared to other works, by analyzing the experimental results obtained in terms of accuracy, iteration times, solution speed, and complexity.