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

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

doi:10.1186/s10033-022-00676-6

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.

Highlights

The ever-increasing advancement of unmanned vehicle technology, especially multivehicle cooperative technology, will significantly promote the development of future intelligent transportation systems and unmanned combat systems and will affect future travel modes and combat patterns [1,2,3]
5 Experimental Results and Discussion To verify the effect of the driver-behaviorbased transferable motion primitive (DBTMP) A* motion-planning algorithm proposed in this study, the wheeled Ackermann-steering vehicle and tracked skid-steering vehicle were chosen, and the corresponding tests were conducted in both the simulation environment and the real environment
5.1 motion primitives (MPs) Library Offline Generation The MP libraries for both platforms used a unified optimization framework during the generation process, and only the details of the vehicle characteristic constraints were changed during the platform transformation process

Summary

Introduction

The ever-increasing advancement of unmanned vehicle technology, especially multivehicle cooperative technology, will significantly promote the development of future intelligent transportation systems and unmanned combat systems and will affect future travel modes and combat patterns [1,2,3]. Motion planning is a crucial component in the unmanned vehicle system framework. Its main function is to generate a reference trajectory that satisfies the constraints of the environment and the vehicle itself [4,5,6]. The essence of MP generation is to solve a set of boundary value problems, that is, to generate a set of paths connecting different target states [11]. The difficulty in solving the above problems lies in the segmentation strategy of the state space and the form of the curve connecting the start and end states. Graph searchbased motion-planning algorithms, such as the typical

Methods

Results

Conclusion