A Real-Time Robust On-Vehicle Generic Obstacle and Lane Detection System Based on Computer Vision Technique

• Main Authors: Tze-Chiuan Lai, 賴則全
• Other Authors: 吳炳飛
• Format: Others
• Language: en_US
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/d2p52n

碩士 === 國立交通大學 === 電機與控制工程系所 === 93 === As the traffic is becoming more and more serious in most developed countries, a lot of researches about the intelligent transportation system (ITS) have been paid attention in recent years. Above all, one of the most promoting topics for the ITS applications is concerning the smart vehicles. The fundamental function of the smart vehicle is the generic obstacle and lane detection system, which can warn the driver or provide the road information for the unmanned vehicle. In this thesis the techniques of image processing and computer vision are applied to the detection system. Two monochromatic CCD cameras are mounted top and bottom on the vehicle, and the road image captured by the top camera is segmented by thresholding the histogram. After that, the quasi-horizontal boundaries formed by the interconnection of two different segments are detected in order, and each detected boundary could belong to either the ground or the obstacle. The criterion to distinguish between them is to predict the corresponding ground and obstacle boundaries in the bottom image by the stereo vision, and to compute the normalized correlation coefficients of the detected boundary in the top image with respect to the ground and obstacle boundaries in the bottom image respectively. The detected boundary in the top image belongs to the obstacle if the normalized correlation coefficient associated with the obstacle is larger than that associated with the ground. Thus the road image can be divided into the ground and obstacle parts. On the other hand, a single monochromatic CCD camera is used in the lane detection system to detect the lane markings. Based on the geometric lane model, the algorithm of lane detection proposed in this thesis can generate a robust result. Besides, the detection region of interest can be estimated to narrow the searching area and to reduce the computational load. Eventually, the 3-D lane geometry is reconstructed to update the road inclination and lane width. Therefore the proposed algorithm is available in the case of non-flat roads. The lane detection system proposed in this thesis has been successfully verified on the expressway and freeway. On the PC platform of 2.6-GHz CPU and 512-MB RAM, the average time of lane detection is less than 1 ms per frame. In addition, the lane detection system can be treated as the vision system of the automatic vehicle by integrating the controller of the steering wheel. This work has been implemented on the experimental car, TAIWAN iTS-1, running on the expressway and freeway with velocities of 90 km/hr and 110 km/hr respectively. TAIWAN iTS-1 is the first smart car in Taiwan capable of hand-free driving on the real road, which verifies the practicability and robustness of the proposed lane detection system.