东北大学学报(自然科学版) ›› 2023, Vol. 44 ›› Issue (8): 1078-1088.DOI: 10.12068/j.issn.1005-3026.2023.08.003

• 信息与控制 • 上一篇    下一篇

面向人机共驾决策的实时驾驶能力评价算法

苏卫星1, 薛凤1, 温永刚2, 刘芳1   

  1. (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.天津商业大学 宝德学院, 天津300384)
  • 发布日期:2023-08-15
  • 通讯作者: 苏卫星
  • 作者简介:苏卫星(1980-),男,辽宁沈阳人,天津工业大学教授.
  • 基金资助:
    国家重点研发计划项目(2021YFB2501800); 国家自然科学基金资助项目(61802280,61806143); 天津市科技计划项目(21YDTPJC00130); 天津市自然科学基金资助项目(18JCQNJC77200).

Real-Time Driving Ability Evaluation Algorithm for Human-Machine Co-driving Decision

SU Wei-xing1, XUE Feng1, WEN Yong-gang2, LIU Fang1   

  1. 1. Tianjin Key Laboratory of Autonomous Intelligence Technology and Systems, Tiangong University, Tianjin 300387, China; 2. Boustead College, Tianjin University of Commerce, Tianjin 300384, China.
  • Published:2023-08-15
  • Contact: LIU Fang
  • About author:-
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摘要: 面向智能辅助驾驶系统人机共驾决策问题对于驾驶人实时驾驶能力评估的需求,考虑现有驾驶评价的单一性问题,提出综合考虑驾驶技能、驾驶状态以及驾驶风格等驾驶人实时驾驶能力评价算法.该算法在提出驾驶能力具有相对性和连续性基础上,首先提出基于高斯核函数的客观熵权驾驶技能相对评价模型、基于时间尺度的驾驶状态相对评价模型以及基于无监督决策分类树的驾驶风格软划分模型;其次,提出具有“惩罚”与“肯定”机制的驾驶能力评价机制及评价模型,实现满足人机共享决策控制需求的实时驾驶能力评价.最后,通过实验比较分析可知,本文提出的评价算法能够满足人机共享系统对于驾驶人驾驶能力评价实时性、客观性、全面性的需求.

关键词: 驾驶能力评价;驾驶风格;高斯核函数;无监督决策树;人机共驾系统

Abstract: To meet the needs of real-time driving ability evaluation for human-machine co-driving decision problems for intelligent assisted driving systems, a method for real-time driving ability evaluation of drivers considering driving skill, driving state and driving style is proposed taking into account the unicity problem of existing driving evaluation researches.Based on the relative and continuous attributes of driving ability, firstly, an objective entropy-weighted relative evaluation model of driving skill is proposed based on the Gaussian kernel function, the relative evaluation model of driving state based on the time scale, and the soft classification model of driving style based on unsupervised decision classification tree. Secondly, a real-time driving ability evaluation mechanism and evaluation model with“punishment”and“affirmation”mechanisms are proposed to achieve real-time driving ability evaluation that meets the needs of human-machine shared decision control.Finally, the experimental comparison analysis shows that the proposed evaluation algorithm can meet the real-time, objective, and comprehensive requirements of human-machine co-driving decision control for drivers driving ability evaluation.

Key words: driving ability evaluation; driving style; Gaussian kernel function; unsupervised decision tree; human-machine co-driving system

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