Chinese Journal of Mechanical Engineering >

2022 , Vol. 35 >Issue 1: 9 - 9

DOI: https://doi.org/10.1186/s10033-022-00676-6

Advanced Transportation Equipment

Generation and Selection of Driver-Behavior-Based Transferable Motion Primitives

  • Haijie Guan ,
  • Boyang Wang ,
  • Jiaming Wei ,
  • Yaomin Lu ,
  • Huiyan Chen ,
  • Jianwei Gong
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  • 1. School of Mechanical Engineering, Beijing Institute of Technology, Beijing, 100081, China;
    2. School of Artificial Intelligence, Peking University, Beijing, China

Received date: 2020-08-09

  Revised date: 2021-12-01

  Online published: 2022-06-30

Supported by

Supported by National Natural Science Foundation of China (Grant Nos. 91420203 and 61703041)

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Abstract

To integrate driver experience and heterogeneous vehicle platform characteristics in a motion-planning algorithm, based on the driver-behavior-based transferable motion primitives (MPs), a general motion-planning framework for offline generation and online selection of MPs is proposed. Optimal control theory is applied to solve the boundary value problems in the process of generating MPs, where the driver behaviors and the vehicle motion characteristics are integrated into the optimization in the form of constraints. Moreover, a layered, unequal-weighted MP selection framework is proposed that utilizes a combination of environmental constraints, nonholonomic vehicle constraints, trajectory smoothness, and collision risk as the single-step extension evaluation index. The library of MPs generated offline demonstrates that the proposed generation method realizes the effective expansion of MP types and achieves diverse generation of MPs with various velocity attributes and platform types. We also present how the MP selection algorithm utilizes a unique MP library to achieve online extension of MP sequences. The results show that the proposed motion-planning framework can not only improve the efficiency and rationality of the algorithm based on driving experience but can also transfer between heterogeneous vehicle platforms and highlight the unique motion characteristics of the platform.

Key words： Autonomous vehicle; Motion planning; Motion primitives; Driver behavior; Heterogeneous vehicle platform

Cite this article

Haijie Guan , Boyang Wang , Jiaming Wei , Yaomin Lu , Huiyan Chen , Jianwei Gong . Generation and Selection of Driver-Behavior-Based Transferable Motion Primitives[J]. Chinese Journal of Mechanical Engineering, 2022 , 35(1) : 9 -9 . DOI: 10.1186/s10033-022-00676-6

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