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基于视觉的缩微智能车车道检测与控制
引用本文: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, 2015, 49(8): 1159-1167
作者姓名:王进  赵蕊  曹宝林  邓欣  陈乔松
作者单位:(重庆邮电大学 计算智能重庆市重点实验室, 重庆 400065)
基金项目: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
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  
收稿时间:2014-05-22

Lane Detection and Steering Control of Vision-Based Micro-Intelligent Vehicle
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(8): 1159-1167
Authors:WANG Jin  ZHAO Rui  CAO Bao lin  DENG Xin  CHEN Qiao song
Affiliation:(Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Chongqing 400065, China)
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.
Keywords:   intelligent transportation; micro-intelligent vehicle; autonomous driving; road lane detection; vehicle control  
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