| Summary: | 碩士 === 國立臺灣大學 === 資訊工程學研究所 === 90 === Intelligent Transportation System (ITS) has been studied for many years all over the world. The goal of such system is to apply advanced technologies to improve the safety and efficiency of surface transportation system. By cooperating with different kinds of infrastructures and sensor technologies, ITS can help to improve the traffic efficiency, reduce the accidents, and decrease the consumption of energy.
Autonomous driving system is one part of the ITS. Such a system can assist to prevent the traffic accidents caused by the negligence of the driver. Obstacle detection and warning mechanism plays an important role in this research field.
Active sensors and passive sensors are used to achieve this goal in many researches. In the proposed system, we adopt the computer vision technology because of its large detecting range and abundant information when compared with other kinds of sensors.
Three categories of obstacles are defined in the proposed system: Pedestrian, Vehicle and Others. Due to different characteristics of these obstacles, we adopt different methods for detection of different kinds of obstacles.
For detection of pedestrian, the potential pedestrian regions are extracted via a simplified fast stereovision method. A template database composed of different gaits is reorganized by adopting the Genetic K-Means Algorithm (GKA) for the verification of pedestrian. The potential regions are compared only with the representative templates from different clusters to improve the efficiency and the M-estimation Hausdorff Distance is used as the metric. On the other hand, the potential locations of vehicle are extracted by the search of their dark underneath. Symmetry properties and edge ratios of these regions are then checked for verification.
After we obtain the locations of these obstacles, their color histogram and geometric positions are recorded for future tracking. A simple extrapolation method is used to predict the next locations of the tracked obstacles. These predicted locations are verified by the concept of intersection of color histograms.
The proposed system is suitable for both the simplified environment such as freeway and the urban environment with complex background. Thus, the result here improves traffic safety not only for drivers, but also for all pedestrians on the road.
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