基于视觉的缩微智能车车道检测与控制

上海交通大学学报（自然版） ›› 2015, Vol. 49 ›› Issue (08): 1159-1167.

• 自动化技术、计算机技术 • 上一篇下一篇

基于视觉的缩微智能车车道检测与控制

王进，赵蕊，曹宝林，邓欣，陈乔松

(重庆邮电大学计算智能重庆市重点实验室，重庆 400065)

收稿日期:2014-05-22 出版日期:2015-08-31 发布日期:2015-08-31
基金资助:
国家自然科学基金项目（61203308，61403054），重庆市自然科学基金项目（cstc2014jcyjA40001，cstc2013jcyjA40063），重庆教委科学技术研究项目（自然科学类）（KJ1400436）资助

Lane Detection and Steering Control of Vision-Based Micro-Intelligent Vehicle

WANG Jin,ZHAO Rui,CAO Baolin,DENG Xin,CHEN Qiaosong

(Chongqing Key Laboratory of Computational Intelligence， Chongqing University of Posts and Telecommunications, Chongqing 400065, China)

Received:2014-05-22 Online:2015-08-31 Published:2015-08-31

摘要/Abstract

摘要：

摘要：针对智能车辆自主驾驶行为研究，提出并实现了一种基于视觉导航的缩微智能车系统.从软硬件架构、数据通信方式上介绍了缩微智能车系统的整体设计，并针对缩微道路交通环境中传统车道线检测技术易受光照变化影响的问题，提出一种灰度形态学TopHat变换与亮度轮廓扫描方法相结合的车道线检测算法.为分析智能车在缩微交通环境下的自主驾驶表现，设计了一种模拟驾驶员转向行为的舵机模糊控制算法.实验结果表明，该方法可以有效模拟车辆在真实道路交通环境中的自主驾驶行为，为智能交通系统研究提供了一种新思路.

关键词: 智能交通, 缩微智能车, 自主驾驶, 车道线检测, 车辆控制

Abstract:

Abstract： To study the autonomous driving behaviour of intelligent vehicles, a visual navigationbased microintelligent vehicle system was proposed and implemented in this paper. The frame of the proposed system was introduced from the aspects of the hardware/software architecture and the data communication scheme. The performance of the traditional lane detection method is very limited in the microroad traffic environment with illumination variations. In the proposed approach, a gray scale morphology tophat transformation and brightness profile scanbased lane detection method was introduced to conquer this problem. To analyze the autonomous driving behaviour of intelligent vehicles in the smallscale traffic environment, a fuzzybased steering control algorithm was designed for simulating the behaviour of drivers. The experimental results show that the proposed scheme is able to effectively simulate the autonomous driving behaviour of vehicles in the real environment, and provides a new approach for the research of intelligent transportation system.

Key words: , intelligent transportation； micro-intelligent vehicle； autonomous driving； road lane detection； vehicle control

中图分类号:

TP 391

王进，赵蕊，曹宝林，邓欣，陈乔松. 基于视觉的缩微智能车车道检测与控制[J]. 上海交通大学学报（自然版）, 2015, 49(08): 1159-1167.

WANG Jin,ZHAO Rui,CAO Baolin,DENG Xin,CHEN Qiaosong. Lane Detection and Steering Control of Vision-Based Micro-Intelligent Vehicle[J]. Journal of Shanghai Jiaotong University, 2015, 49(08): 1159-1167.

参考文献

［1］Gechter F， Contet J M， Galland S， et al. Virtual intelligent vehicle urban simulator: Application to vehicle platoon evaluation［J］. Simulation Modelling Practice and Theory，2012，24(1)：103114.

［2］Yu Y， El Kamel A， Gong G. Multiagent based architecture for virtual reality intelligent simulation system of vehicles［C］∥Proceedings of the 2013 10th IEEE International Conference on Networking, Sensing and Control (ICNSC). Piscataway：IEEE Press，2013：597602.

［3］Li D Y， Wang X D， He W， et al. Study on interaction behaviors of microautonomous vehicles［C］∥ Proceedings of the 2011 10th International Symposium on Autonomous Decentralized Systems (ISADS). Los Alamitos：IEEE Computer Society Press，2011：399406.

［4］La H M， Lim R S， Du J， et al. A smallscale research platform for intelligent transportation systems［C］∥ Proceedings of the 2011 IEEE International Conference on Robotics and Biomimetics (IEEEROBIO 2011). Piscataway：IEEE Press，2011：13731378.

［5］Oliveira M， Santos V， Sappa A. Short term path planning using a multiple hypothesis evaluation approach for an autonomous driving competition［EB/OL］. (20121107)［20130701］. http:∥ppniv 12.irccyn.ecnantes.fr/paper/4Oliveira.pdf.

［6］毕建权，陆耿，王京春，等. 缩微智能车软件系统设计与实现［J］. 计算机工程与应用，2013，49(1)：234238.

BI Jianquan， LU Geng， WANG Jingchun， et al. Design and implementation of software system for autonomous microvehicles［J］. Computer Engineering and Applications，2013，49(1)：234238.

［7］Lakhotia A， Golconda S， Maida A， et al. CajunBot: Architecture and algorithms［J］. Journal of Field Robotics，2006，23(8)：555578.

［8］Bai X Z， Zhou F G. A unified form of multiscale TopHat transform based algorithms for image processing［J］. OptikInternational Journal for Light and Electron Optics，2013，124(13)：16141619.

［9］金辉，吴乐林，陈慧岩，等. 结构化道路车道线识别的一种改进算法［J］. 北京理工大学学报，2007，27(6)：501505.

JIN Hui， WU Lelin， CHEN Huiyan， et al. An improved algorithm for the lane recognition of structured road［J］. Transactions of Beijing Institute of Technology，2007，27(6)：501505.

［10］Truong Q B， Lee B R. New lane detection algorithm for autonomous vehicles using computer vision［C］∥ Proceedings of the 2008 IEEE International Conference on Control, Automation and Systems (ICCAS 2008). Piscataway：IEEE，2008：12081213.

［11］Fan C， Zhang R， Xu J B， et al. Research on the lane detection algorithm based on crossentropy［J］. Journal of Computational Information Systems，2013，9(7)：26672674.

［12］陈龙，李清泉，毛庆洲. 基于成像模型的车道线检测与跟踪方法［J］. 中国公路学报，2012，24(6)：96102.

CHEN Long， LI Qingquan，MAO Qingzhou. Lane detection and following algorithm based on imaging Mode［J］. China Journal of Highway and Transport，2012，24(6)：96102.

［13］Mohamed A. Caltech lanes dataset［EB/OL］. (20110629) ［20130701］. http:∥www.vision.caltech.edu/malaa/datasets/caltechlanes/.

［14］CMU/VASC image database ［EB/OL］. (19900530) ［20130701］. http:∥vasc.ri.cmu.edu/idb/html/road/index.html.

［15］Ren W， Atkins E. Distributed multivehicle coordinated control via local information exchange［J］. International Journal of Robust and Nonlinear Control，2007，17(1011)：10021033.

［16］彭新荣，杨明，谢强德，等. 一种基于可靠预约ALOHA的智能车多车协作通信协议［J］. 上海交通大学学报，2010，44(9)：12111216.

PENG Xinrong，YANG Ming，XIE Qiangde， et al. An intervehicle communication protocol for multivehicle cooperation based on RRALOHA［J］. Journal of Shanghai Jiaotong University，2010，44(9)：12111216.

［17］周俊，姬长英. 智能车辆横向控制研究［J］. 机器人，2003，25(1)：2630.

ZHOU Jun， JI Changying. Lateral control of intelligent vehicle［J］. Robot，2003，25(1)：2630.

基于视觉的缩微智能车车道检测与控制

Lane Detection and Steering Control of Vision-Based Micro-Intelligent Vehicle

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 1

编辑推荐

Metrics

本文评价