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 selec-tion 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.

Haijie Guan;Boyang Wang;Jiaming Wei;Yaomin Lu;Huiyan Chen;Jianwei Gong

School of Mechanical Engineering,Beijing Institute of Technology,Bei-jing 100081,China;School of Artificial Intelligence,Peking University,Beijing,China.

中国机械工程学报

[1]Haijie Guan;Boyang Wang;Jiaming Wei;Yaomin Lu;Huiyan Chen;Jianwei Gong-.Generation and Selection of Driver-Behavior-Based Transferable Motion Primitives)[J].中国机械工程学报,2022(01):208-222

Primitives

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