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Learning and generating vehicle motion primitives from human-driving data

Published online by Cambridge University Press:  04 February 2025

Gengxin Li Open the ORCID record for Gengxin Li [Opens in a new window] ,
Jianru Xue ,
Bohua Zhang ,
Kang Zhao  and
Zhongxing Tao
Gengxin Li
Affiliation:
Xi’an Jiaotong University, Institute of Artificial Intelligence and Robotics, Xi’an, China
Jianru Xue*
Affiliation:
Xi’an Jiaotong University, Institute of Artificial Intelligence and Robotics, Xi’an, China
Bohua Zhang
Affiliation:
Xi’an Jiaotong University, Institute of Artificial Intelligence and Robotics, Xi’an, China
Kang Zhao
Affiliation:
Xi’an Jiaotong University, Institute of Artificial Intelligence and Robotics, Xi’an, China
Zhongxing Tao
Affiliation:
Northwest Normal University, LanZhou, China
*
Corresponding author: Jianru Xue; Email: [email protected]
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Abstract

Motion primitives play an important role in motion planning for autonomous vehicles, as they effectively address the sampling challenges inherent in nonholonomic motion planning. Employing motion primitives (MPs) is a widely accepted approach in nonholonomic motion planning based on sampling. This study specifically addresses the problem of learning from human-driving data to create human-like trajectories from predefined start-to-end states, which then serve as MP within the sampling-based nonholonomic motion planning framework. In this paper, we propose a deep learning-based method for generating MP that capture human-driving trajectory data features. By processing human-driving trajectory data, we create a Motion Primitive dataset that uniformly covers typical urban driving scenarios. Based on this dataset, a vehicle model long short-term memory neural network model is constructed to learn the features of the human-driving trajectory data. Finally, a framework for the generation of MP for practical applications is given based on this neural network. Our experiments, which focus on the dataset, the MMP generation network, and the generation process, demonstrate that our method significantly improves the training efficacy of the MP generation network. Additionally, the MP generated by our method exhibit higher accuracy compared to traditional methods.

Keywords

autonomous vehiclemotion planningmotion primitivesLSTMGMM
Type
Research Article
Information
Robotica , First View , pp. 1 - 19
Copyright
© The Author(s), 2025. Published by Cambridge University Press

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