Regionalized Decision Algorithm for Human-Machine Shared Control Based on Gaussian Hidden Markov Model

LIU Fang; ZHU Tian-he; SU Wei-xing; LIU Yang

doi:10.12263/DZXB.20211438

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Regionalized Decision Algorithm for Human-Machine Shared Control Based on Gaussian Hidden Markov Model

PAPERS | 更新时间：2025-12-08

- Regionalized Decision Algorithm for Human-Machine Shared Control Based on Gaussian Hidden Markov Model
- ACTA ELECTRONICA SINICA Vol. 50, Issue 11, Pages: 2659-2667(2022)
- 作者机构：
  
  1.天津工业大学天津市自主智能技术与系统重点实验室，天津 300387
  2.华晨宝马汽车有限公司整车开发部，辽宁沈阳 110098
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20211438
  CLC： TP391.41;U491.25
- Received：20 October 2021，
  
  Revised：2022-03-31，
  
  Published：25 November 2022
- 稿件说明：
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刘芳,朱天贺,苏卫星等.基于高斯隐马尔可夫模型的人机共享控制区域化决策算法[J].电子学报,2022,50(11):2659-2667.

LIU Fang,ZHU Tian-he,SU Wei-xing,et al.Regionalized Decision Algorithm for Human-Machine Shared Control Based on Gaussian Hidden Markov Model[J].ACTA ELECTRONICA SINICA,2022,50(11):2659-2667.
刘芳,朱天贺,苏卫星等.基于高斯隐马尔可夫模型的人机共享控制区域化决策算法[J].电子学报,2022,50(11):2659-2667. DOI： 10.12263/DZXB.20211438.

LIU Fang,ZHU Tian-he,SU Wei-xing,et al.Regionalized Decision Algorithm for Human-Machine Shared Control Based on Gaussian Hidden Markov Model[J].ACTA ELECTRONICA SINICA,2022,50(11):2659-2667. DOI： 10.12263/DZXB.20211438.

摘要

针对伺服级共享控制决策中权衡安全性、干预度与驾驶体验的问题，提出基于高斯隐马尔可夫模型（Gaussian Hidden Markov Model，GHMM）的人机共享控制区域化决策算法.此算法利用高斯分布函数表征驾驶人的实时相对驾驶能力；利用区域化的高斯矢量环境风险场量化模型表征不同环境区域的环境风险值以及其模糊风险等级；最后综合驾驶人绝对能力、驾驶状态以及环境风险实现人机共享控制中控制权的高可靠、合理分配.实验表明，本文提出的人机共享区域化决策模型能够在考虑驾驶人相对能力及环境风险源所在方位的基础上给予较为合理的控制权柔性分配方案，有效降低风险至智能驾驶模型可控范围内.

Abstract

To address the problem of trade-off between safety

intervention and driving experience in servo-level shared control decision-making

a regionalized human-machine shared control decision algorithm based on Gaussian hidden Markov model(GHMM) is proposed. In this algorithm

Gaussian distribution function was applied to characterize the real-time relative driving ability of the driver and regionalized Gaussian vector risk field quantification model was applied to characterize the environmental risk and fuzzy risk levels in different regions. Finally the human-machine shared control decision integrated driver's absolute ability

driving state and environmental risk to achieve a highly reliable and reasonable allocation of control right. The experiments show that regionalized human-machine shared control decision algorithm proposed in this paper can give a more reasonable flexible allocation scheme of control right based on the relative ability of drivers and the orientation of risk sources

and effectively reduce the risk within the controllable range of the intelligent driving model.

关键词

Keywords

references

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