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Computational Efficient Motion Planning Method for Automated Vehicles Considering Dynamic Obstacle Avoidance and Traffic Interaction.

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Zhang, Yuxiang 
Lv, Jidong 
Gao, Bingzhao 
Chu, Hongqing 


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.


Peer reviewed: True


Article, autonomous vehicles, trajectory planning, model predictive control

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Sensors (Basel)

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International Technology Cooperation Program of Science and Technology Commission of Shanghai Municipality (21160710600)
Shanghai Pujiang Program (21PJD075)
China Automobile Industry Innovation and Development Joint Fund (U1864206)