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How to integrate human factors into the motion planning system is of greatsignificance for improving the acceptance of intelligent vehicles. Decomposingmotion into primitives and then accurately and smoothly joining the motionprimitives (MPs) is an essential issue in the motion planning system.Therefore, the purpose of this paper is to regenerate and join the learned MPsin the library. By applying a representation algorithm based on the modifieddynamic movement primitives (DMPs) and singular value decomposition (SVD), ourmethod separates the basic shape parameters and fine-tuning shape parametersfrom the same type of demonstration trajectories in the MP library. Moreover,we convert the MP joining problem into a re-representation problem and use thecharacteristics of the proposed representation algorithm to achieve an accurateand smooth transition. This paper demonstrates that the proposed method caneffectively reduce the number of shape adjustment parameters when the MPs areregenerated without affecting the accuracy of the representation. Besides, wealso present the ability of the proposed method to smooth the velocity jumpwhen the MPs are connected and evaluate its effect on the accuracy of trackingthe set target points. The results show that the proposed method can not onlyimprove the adjustment ability of a single MP in response to different motionplanning requirements but also meet the basic requirements of MP joining in thegeneration of MP sequences.

Regeneration and Joining of the Learned Motion Primitives for Automated Vehicle Motion Planning Applications