本研究以基於視覺回授之方式來開發車輛之自動駕駛系統，主要可分為三個重點技術，首先是使用光學鏡頭來建構車道維持系統，透過拍攝車輛前方的道路情況，以灰階化、二值化、霍爾直線轉換等影像處理方法偵測出標線位置，再以形態學將車道標線從影像座標轉換至二維空間座標，計算出車道的方向和曲率，以此作為車輛行駛之基礎路徑，並計算出車輛與基礎路徑之偏移量；為了使車輛可以平滑的沿著基礎路徑行駛並對障礙物進行迴避，本研究以雷射掃描儀偵測車輛前方是否存在障礙物，並基於第一部分所獲得之基礎路徑資訊，以三次方程式規劃車輛之行進路線，進而得到車輛之轉向角命令；最後以嵌入式系統的方式，利用二分之一實車大小之電動車進行實際測試與驗證，使用四輪和線控轉向系統回傳之編碼器值回授車輛狀態，實際路況試驗上設計直線與曲線等路段，並於路徑上放置障礙物，實際測試自動駕駛系統之駕駛路徑及避障能力。

In this thesis, a vision-based active driving assistance system is proposed. It can be classified into three parts. The first part is to develop the lane keeping system (LKS) by using camera to capture the image in front of the vehicle. By using image processing method, such as gray-scalar, binarization, and Hough transform, we can capture the image coordinate of the lane. In order to calculate lane information, transforming the image coordinate to Cartesian coordinate by homography is needed. The second part is to plan a smooth path based on lane information and obstacle information. A laser range scanner is used to search the obstacle location on the road for collision avoidance. Based on these data, a cubic spline path is generated in real-time, which can provide vehicle a continuous and smooth path. The last part, the system is embedded in an electric vehicle which size is half of normal vehicle to verify its dynamic performance. The states of vehicle are feed-back by using the encoder of four wheels and SBW respectively. The experiments of self-driving mission include straight and curve road with obstacle on the road were planned. The experimental results show that the electric vehicle has good lane keeping ability and obstacle avoiding capability.

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