主办:陕西省汽车工程学会
ISSN 1671-7988  CN 61-1394/TH
创刊:1976年

Automobile Applied Technology ›› 2022, Vol. 48 ›› Issue (2): 51-61.DOI: 10.16638/j.cnki.1671-7988.2023.02.010

• Intelligent Connected Vehicle • Previous Articles    

Online Driver Intention Recognition Based on CRF

LI Xuehan, CHEN Huanming* , HUA Hang   

  1. College of Mechanical and Electrical Engineering
  • Online:2023-01-30 Published:2023-01-30
  • Contact: CHEN Huanming

基于 CRF 的驾驶员意图在线识别

李雪涵,陈焕明*,华 航   

  1. 青岛大学 机电工程学院
  • 通讯作者: 陈焕明
  • 作者简介:李雪涵(1997—),男,硕士研究生,研究方向为驾驶意图识别,E-mail:2020025576@qdu.edu.cn。 通信作者:陈焕明(1986—),男,博士,讲师,研究方向为车辆系统动力学仿真与控制,E-mail:qdchm@qdu.edu.cn。

Abstract: In order to improve the decision-making accuracy of driving assistance system, this paper establishes an online driver intention recognition system based on conditional random field (CRF), which provides an important reference for driving assistance system. In this paper, deep label distribution learning (DLDL) is used to recognize the head posture of drivers to judge their observation targets. Experimental scenes are created on the driving simulator. The driver operation data are collected, divided according to the operation and input to CRF for training, and drivers' intention to change lanes is recognized. Through experimental comparison, the recognition rate is above 96%, and the driver's lane change intention can be recognized before 5 s. On this basis, online recognition is realized to meet the practical application requirements.

Key words: Deep label distribution learning; Head posture; Conditional random field; Online driver intention recognition

摘要: 为提升驾驶辅助系统决策准确率,文章建立一种基于条件随机场(CRF)的驾驶员意 图在线识别系统,为驾驶辅助系统提供重要参考。文章采用深度标签分布学习(DLDL)识别 驾驶员的头部姿态判断其观察目标,在驾驶模拟器上创建实验场景,采集驾驶员操作数据, 按照操作划分并输入至 CRF 进行训练,识别驾驶员的换道意图。通过实验对比,识别率在 96% 以上,能够在 5 s 前识别驾驶员的换道意图。在此基础上实现在线识别,满足实际应用要求。

关键词: 深度标签分布学习;头部姿态;条件随机场;驾驶意图在线识别