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Nonlinear Model Predictive Control (NMPC) is effective for local planning of automated vehicles, especially when there exist dynamical objects and multipe requirements. But it requires many computation resources for numerical optimization, which limits its practical application becase of the limited power of onboard unit. To extend the application range of the NMPC based local planner, the coupled nonlinear vehicle dynamics model is adopted based on the numerical analysis, which conversely requires much more discretization poits for acceptable accuracy. For better computation efficiency, Lagrange polynomials are used to discretize the vehicle dynamics model and objective function with less points and fine numerical accuracy. Furthermore, an adaptive strategy is designed to determine the order of Lagrange polynomials according to running state by numerical analysis of discretization error. Both acceleration effect and performance of the local planner designed by NMPC are validated by experimental tests under scenarios with multiple dynamical obstacles. The test results show that compared with the original one the accuracy and efficiency are improved by 74% and 60%, respectively. [ABSTRACT FROM AUTHOR]

In complex driving scenarios, automated vehicles should behave reasonably and respond adaptively with high computational efficiency. In this paper, a computational efficient motion planning method is proposed, which considers traffic interaction and accelerates calculation. Firstly, the behavior is decided by connecting the points on the unequally divided road segments and lane centerlines, which simplifies the decision-making process in both space and time span. Secondly, as the dynamic vehicle model with changeable longitudinal velocity is considered in the trajectory generation module, the C/GMRES algorithm is used to accelerate the calculation of trajectory generation and realize on-line solving in nonlinear model predictive control. Meanwhile, the motion of other traffic participants is more accurately predicted based on the driver's intention and kinematics vehicle model, which enables the host vehicle to obtain a more reasonable behavior and trajectory. The simulation results verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]