Detecting Driver Normal and Emergency Lane-Changing Intentions With Queuing Network-Based Driver Models

被引：31

作者：

Bi, Luzheng ^{[1
]}

Wang, Cuie ^{[1
]}

Yang, Xuerui ^{[1
]}

Wang, Mingtao ^{[1
]}

Liu, Yili ^{[2
]}

机构：

[1] Beijing Inst Technol, Sch Mech Engn, Beijing 100081, Peoples R China

[2] Univ Michigan, Dept Ind & Operat Engn, Ann Arbor, MI 48109 USA

来源：

INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION | 2015年 / 31卷 / 02期

基金：

中国国家自然科学基金;

关键词：

DISTRACTION TASK; BEHAVIOR; PREDICTION;

D O I：

10.1080/10447318.2014.986638

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Driver intention detection is an important component in human-centric driver assistance systems. This article proposes a novel method for detecting driver normal and emergency left-or right-lane-changing intentions by using driver models based on the queuing network cognitive architecture. Driver lane-changing and lane-keeping models are developed and used to simulate driver behavior data associated with 5 kinds of intentions (i.e., normal and emergency left-or right-lane-changing and lane-keeping intentions). The differences between 5 sets of simulated behavior data and the collected actual behavior data are computed, and the intention associated with the smallest difference is determined as the detection outcome. The experimental results from 14 drivers in a driving simulator show that the method can detect normal and emergency lane-changing intentions within 0.325 s and 0.268 s of the steering maneuver onset, respectively, with high accuracy (98.27% for normal lane changes and 90.98% for emergency lane changes) and low false alarm rate (0.294%).

引用

页码：139 / 145

页数：7