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.
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 realtime 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.
作者
刘芳
朱天贺
苏卫星
刘阳
LIU Fang;ZHU Tian-he;SU Wei-xing;LIU Yang(Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems,Tiangong University,Tianjin 300387,China;BBT-E-6 Complete Vehicle,BMW Brilliance Automotive Ltd.,Shenyang,Liaoning 110098,China)
出处
《电子学报》
EI
CAS
CSCD
北大核心
2022年第11期2659-2667,共9页
Acta Electronica Sinica
基金
国家重点研发计划(No.2021YFB2501800)
天津市研究生科研创新项目(No.2021YJSO2S17)
国家自然科学基金(No.61802280,No.61806143,No.61772365,No.41772123)
天津市技术创新引导专项(基金)(No.21YDTPJC00130)。
关键词
人机共驾
柔性驾驶控制权分配
行车风险场
驾驶人能力评价
隐马尔可夫模型
矢量风险场
man-machine shared driving
flexible driving control distribution
driving risk field
driving ability evaluation
hidden Markov model
vector risk field
