交通运输系统工程与信息

交通运输系统工程与信息 ›› 2021, Vol. 21 ›› Issue (1): 41-47.

所属专题： 自动驾驶技术

• 智能交通系统与信息技术 • 上一篇    下一篇

驾驶人“感知-决策-操控”行为模型

冯树民*1，黄秋菊1, 2，张宇2，赵琥1   

  1. 1. 哈尔滨工业大学，交通科学与工程学院，哈尔滨 150010；2. 哈尔滨职业技术学院，汽车学院，哈尔滨 150081
  • 收稿日期:2020-07-24 修回日期:2020-09-22 出版日期:2021-02-25 发布日期:2021-02-25
  • 作者简介:冯树民(1973- )，男，黑龙江哈尔滨人，教授，博士。
  • 基金资助:

    国家重点研发计划项目/National Key Research and Development Program Project of China(2017YFC0803901)。

Driver's Perception-Decision-Control Model

FENG Shu-min*1, HUANG Qiu-ju1, 2, ZHANG Yu2, ZHAO Hu1   

  1. 1. School of Transportation Science and Engineering, Harbin Institute of Technology, Harbin 150010, China; 2. School of Automobile, Harbin Vocation and Technical College, Harbin 150081, China
  • Received:2020-07-24 Revised:2020-09-22 Online:2021-02-25 Published:2021-02-25

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摘要：

为准确模拟驾驶人跟车行为，提出基于隐马尔可夫模型(Hidden Markov Model，HMM)的驾驶人“感知-决策-操控”行为模型。建立描述驾驶意愿的HMM模型，模拟驾驶人感知过程，获得期望的车间距；预测模块模拟驾驶人根据交通环境和自身生理、心理状态预测车辆未来轨迹，即决策过程；优化模块描述驾驶人为使预测的车辆轨迹跟踪上期望的车辆间距而采取的操控汽车的执行动作，即操控过程。上述3个模块的滚动过程实现了对驾驶人跟车行为的模拟。利用自然驾驶数据进行算例分析，结果表明，本文模型预测车间距平均误差仅为1.47%，证明了所建模型的有效性及准确性。本文为驾驶行为建模方法的理论研究和应用拓宽了思路。

关键词: 交通工程, 驾驶行为模型, HMM理论, 自然驾驶状态跟车行为, 自动驾驶

Abstract:

This paper proposes a Hidden Markov Model (HMM) based driver perception- decision- manipulation behavior model to simulate the car- following behaviors. The HMM model is used to describe driving intention and simulate the driver's perception process, that is, to obtain the desired vehicle spacing. The prediction module is developed to predict the vehicle trajectory responding to the traffic conditions and driver's psychological status. The prediction module represents driver's decision- making process. The optimization module simulates driver's control actions and adjusts the predicted vehicle spacing to meet the expected vehicle spacing. Driver's perception- decisioncontrol behavior is then simulated through a rolling process of the three proposed sub-modules. The natural driving data were used for empirical analysis and the results indicate the average error of the model is 1.47%, which reflects the effectiveness and accuracy of the model. This paper provides a new perspective for the theoretical research and application of driving behavior modeling.

Key words: traffic engineering, driving behavior model, HMM(Hidden Markov Model) theory, car following in natural, autonomous driving

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