人机协同下辅助驾驶系统的车道保持控制

doi:10.3901/JME.2018.02.169

机械工程学报 ›› 2018, Vol. 54 ›› Issue (2): 169-175.doi: 10.3901/JME.2018.02.169

人机协同下辅助驾驶系统的车道保持控制

李进¹, 刘洋洋¹, 胡金芳²

1. 安徽科技学院机械工程学院滁州 233100;
2. 合肥工业大学汽车与交通工程学院合肥 230009

收稿日期:2016-11-30 修回日期:2017-09-08 出版日期:2018-01-20 发布日期:2018-01-20
通讯作者: 李进(通信作者),男,1982年出生,博士,副教授。主要研究方向为汽车电子与智能车辆。E-mail:region2008@163.com
基金资助:
安徽省高校优秀青年人才支持计划重点（gxyqZD2016215）和国家自然科学基金（51505115）资助项目。

Lane Tracking of Driver Assistance System with Man-machine-coordination

LI Jin¹, LIU Yangyang¹, HU Jinfang²

1. School of Mechanical Engineering, Anhui Science and Technology University, Chuzhou 233100;
2. School of Automobile and Transportation Engineering, Hefei University of Technology, Hefei 230009

Received:2016-11-30 Revised:2017-09-08 Online:2018-01-20 Published:2018-01-20

摘要/Abstract

摘要： 为兼顾驾驶员和辅助驾驶系统在车道保持控制中的优势，根据实时驾驶员操作动作和车辆道路信息对车辆横向安全性进行评价，并对车辆控制权在驾驶员和辅助驾驶系统之间做出实时决策，以实现人机协同控制。在车道识别方面，采用了同帧图像的分区识别、相邻帧图像的车道候选区估计等方法。在车道跟踪控制时，根据车辆横向安全性高低采用不同控制策略，并基于模糊规则确定辅助驾驶控制力度以计算人机协同控制时的实际辅助驾驶控制量。在不同车速和不同道路条件下，采用人工驾驶和人机协同控制两种方式进行车道保持实车试验。试验结果表明，所采用的方法能够有效识别道路车道线，且人机协同控制下的车道跟踪具有较好的精确性和稳定性。

关键词: 车道保持, 辅助驾驶, 辅助驾驶控制力度, 人机协同

Abstract: In order to obtain the advantages of both manual control and assistance control for lane tracking, the vehicle lateral safety grade is evaluated based on driving action and position relationship of the vehicle and the lane. And control right is decided among the driver and the driver assistance system, so the man-machine coordinative control can be carried out. For the lane image recognition, segmental lane detection and candidate lane estimation is adopted. According to different vehicle lateral safety grading, different control strategies are applied. The assistance control index is calculated based on fuzzy control rules, and then the assistance control is performed when the man-machine coordinative control is needed. Under different road conditions, the road experiments are carried out by means of manual driving and man-machine cooperative driving with different vehicle speed. The results show that the lanes can be recognized efficiently, and the vehicle under man-machine coordinative control can track the lanes more accurately and reliably.

Key words: assistance control index, driver assistance system, lane tracking, man-machine coordination

中图分类号:

TP242

李进, 刘洋洋, 胡金芳. 人机协同下辅助驾驶系统的车道保持控制[J]. 机械工程学报, 2018, 54(2): 169-175.

LI Jin, LIU Yangyang, HU Jinfang. Lane Tracking of Driver Assistance System with Man-machine-coordination[J]. Journal of Mechanical Engineering, 2018, 54(2): 169-175.

参考文献

[1] 孟文俊,刘忠强. 视觉导引AGV的路径跟踪控制研究[J]. 控制工程,2014,21(3):321-325. MENG Wenjun,LIU Zhongqiang. Research on the path tracking control for vision-guided AGV[J]. Control Engineering of China,2014,21(3):321-325.
[2] 李进,刘洋洋. 兼顾可靠性和实时性的智能车辆车道保持[J]. 控制工程,2016,23(3):383-388. LI Jin,LIU Yangyang. Real-time and reliable lane recognition and tracking of intelligent vehicle[J]. Control Engineering,2016,23(3):383-388.
[3] KIM Z W. Robust lane detection and tracking in challenging scenarios[J]. IEEE Transactions on Intelligent Transportation System,2008,9(1):16-26.
[4] CHOI H C,PARK J M,CHOI W S,et al. Vision-based fusion of robust lane tracking and forward vehicle detection in a real driving environment[J]. International Journal of Automotive Technology,2012,13(4):653-669.
[5] 王家恩,陈无畏,汪明磊,等. 车辆辅助驾驶系统中的三车道检测算法[J]. 汽车工程,2014,36(11):1378-1385. WANG Jiaen,CHEN Wuwei,WANG Minglei,et al. A three-lane detection algorithm for vehicle assistant driving system[J]. Automotive Engineering,2014,36(11):1378-1385.
[6] 李进,陈无畏. 基于自适应导航参数的智能车辆视觉导航[J]. 农业机械学报,2012,43(6):19-25. LI Jin,CHEN Wuwei. Navigation of vision-guided intelligent vehicle based on adaptive navigation parameters[J]. Transactions of the Chinese Society for Agricultural Machinery,2012,43(6):19-25.
[7] 陈无畏,李进,王檀彬,等. 视觉导航智能车辆的路径跟踪预瞄控制[J]. 机械工程学报,2008,44(10):277-282. CHEN Wuwei, LI Jin, WANG Tanbin,et al. Preview control for road following of vision guided intelligent vehicle[J]. Journal of Mechanical Engineering, 2008,43(10):277-282.
[8] 高志峰,汪渤,周志强,等. 一种鲁棒的非平坦路面车道线检测算法[J]. 北京理工大学学报,2013,33(1):73-78. GAO Zhifeng,WANG Bo,ZHOU Zhiqiang,et al. A robust algorithm for lane detection on unplanar road[J]. Transactions of Beijing Institute of Technology,2013,33(1):73-78.
[9] 李进,陈杰平,徐朝胜,等. 基于动态图像阈值的智能车辆路径导航[J]. 农业机械学报,2013,44(4):39-44. LI Jin,CHEN Jieping,XU Chaosheng,et al. Path tracking of intelligent vehicle based on dynamic image threshold[J]. Transactions of the Chinese Society for Agricultural Machinery,2013,44(4):39-44.
[10] 金立生,方文平,候海晶,等. 基于BP神经网络的车道保持控制系统[J]. 吉林大学学报,2010,40(3):650-654. JIN Lisheng,FANG Wenping,HOU Haijing,et al. Co-simulation of lane keeping control system based on BP neural network[J]. Journal of Jilin University,2010,40(3):650-654.
[11] MARINO R,SCALZI S,ORLANDO G,et al. A nested PID steering control for lane keeping in vision based autonomous vehicles[C]//American Control Conference,June 10-12,2009,St. Louis,MO,USA:IEEE,2009:2885-2890.
[12] CHEN Liangkuang, YANG Chuanhui. Control authority determination of a vehicle lane keeping assist controller[C]//Intelligent Vehicles Symposium,June 3-5,2009,Xi'an:IEEE,2009:1383-1388.
[13] BLASCHKE C, BREYER F, Färber B, et al. Driver distraction based lane-keeping assistance[J]. Transportation Research Part F:Traffic Psychology and Behaviour,2009,12(4):288-299.
[14] 李进,陈杰平,易克传,等. 基于特征融合的视觉导航智能车辆车道保持[J]. 汽车工程,2015,37(5):587-592. LI Jin,CHEN Jieping,YI Kechuan,et al. Lane identification of vision-guided intelligent vehicle based on feature fusion[J]. Automotive Engineering,2015,37(5):587-592.

人机协同下辅助驾驶系统的车道保持控制

Lane Tracking of Driver Assistance System with Man-machine-coordination

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