The Multi-Agent Driving Assistance System

• Main Authors: Cheng-Yen Wu, 吳政衍
• Other Authors: Pau-Lo Hsu
• Format: Others
• Language: zh-TW
• Published: 2004
• Online Access: http://ndltd.ncl.edu.tw/handle/nedx94

碩士 === 國立交通大學 === 電機與控制工程系所 === 92 === The proposed system including three subsystems, the image, the laser scanner, and the multi-agent systems. In the image system, the lane detection is carried out by applying the inverse perspective mapping (IPM) to locate suitable windows for image processing to obtain the road lanes. Then, the on-line detection algorithms are processed to track the lanes efficiently. Also, the driving angle from the lanes are calculated. For detection of the front vehicles, the symmetrical characteristics of the vehicle image can be applied to locate the possible vehicle positions in the image. Furthermore, a simple searching approach can be applied to confirm the detection results. Thus, the lane keeping and collision avoidance can be achieved. In the laser scanner subsystem, the estimation of relative speed between two cars is achieved by applying the 1-dimensional Kalman filter to obtain a reliable D/V curve. Thus, it provides the collision pre-warning time for the driver. However, it is not suitable for two cars not in the same line. A transformation of all measured signals by constructing six virtual lasers scanners is proposed in this thesis so that the present laser scanning system can be applied to a wide range. To integrate both the image and the laser scanner as a driving assistance system, a multi-agent system is proposed to efficiently exchange information from two agents corresponding to the image and the laser scanner, separately. Six conditions are concluded that the image agent is not suitable for car detection alone; moreover, two cases indicate that the laser scanner may face difficulties in detecting the front car alone. Therefore, the multi-agent system is proposed in this thesis to integrate the image agent and the laser scanner agent. With mutual data and information exchange, the processing time is significantly reduced and the false detection is also surpressed. Furthermore, it provides the flexibility for future expansion with more sensors in this system. Finally, a multi-agent driving assistance system for the lane departure detection and the collision avoidance has been realized on a golf car tested successfully Sin the campus of National Chiao Tung University as well as the North Second High Way.