此論文提出了一個結合了道路標線偵測、車輛追蹤，以及距離估計技術的前方防撞輔助系統(FCAAS)。首先，道路標線偵測技術使用RANSAC演算法從被steerable filter處理過的IPM影像中取出道路標線延伸所得的直線，並採用Kalman filter來追蹤取出的直線。再者，車輛追蹤技術用particle filter實作出多重車輛追蹤的方法，此方法會針對由adaboost 分類器偵測到的車輛進行個別的追蹤。本論文改進了particle filter的取樣方式，使得particle filter能夠更準確的框出影像中的車輛，且減少了每次所需取樣的particle數量。除此之外，此論文推導出一個新穎的距離估計(DE)公式來計算自身車輛與其前方車輛的距離。DE公式經過了審慎的驗證，即運用道路標線規定之長度來推算影像中道路標線位置與真實距離的關係。此驗證得以證明DE公式符合在真實環境下的需求。FCAAS透過許多的高速公路實驗影片展現其在實際場景下正確運作的潛力，且符合即時系統的需求，執行速度可達每秒二十二張frames。

This paper proposes a novel forward collision avoidance assist system (FCAAS) containing techniques of lane marking detection, vehicle tracking and distance estimation (DE). First, a lane marking detection technique uses a RANSAC algorithm to extract lines of lane markings, which were previously collected from an Inverse Perspective Mapping (IPM) image filtered by steerable filters. A Kalman filter then tracks the extracted lines accurately and efficiently. Second, a vehicle tracking technique implements a multiple vehicle tracking method using a particle filter, which tracks the vehicles detected by an AdaBoost classifier. An improved particle filter is implemented to predict the next movement of a vehicle and spread the particles near the predicted location of the vehicle instead of originally spreading the particles around the current location of the vehicle. Finally, an innovative DE formula is derived to estimate the distance between the ego vehicle and the front vehicle. The DE formula is verified by setting several standard points in the image, whose locations can be measured according to the regulation of lane markings. As a result, verification of the DE formula demonstrates a robust feasibility in reality. The FCAAS shows its potential in particular scenes through many experimental sequences acquired from highways in the real world. In addition, the FCAAS fits the demand of a real-time speed system with speeds of 22 frames per second.

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