整合前方多車道線與障礙物偵測之研究

Abstract

本文利用影像處理與電腦視覺原理偵測車道線與車輛位置並推算前車距離，同時使用數位訊號處理器(TI DM642)達成行車輔助系統，在高快速公路、市區、隧道場景下均可運作，同時亦可在晴天、陰天、傍晚、夜間、雨天下等複雜環境偵測車道線與估算前方車輛距離。 車道線方面，使用微型低照度CCD攝影機擷取車輛前方影像，配合演算法偵測車道標線位置，建立真實車道模型。依照模型參數適當地預估及縮小搜尋範圍，降低搜尋時間而提高偵測效率。同時藉由模型所推算道路傾斜度與車道寬度適時回授給演算法，即時且精確的修正車道線模型。本文發展出的車道偵測演算法成功地在高快速道路與市區道路驗證，同時與台灣首輛智慧車TAIWAN iTS-1上結合控制系統，做為無人駕駛智慧車的視覺系統，以完成自動駕駛之車道保持與變換車道之任務。 車輛偵測方面，使用上述之車道模型建立車輛基本參數，利用現有車道線設定偵測範圍。而後配合演算法選取多個類似車輛之形體，經由特徵萃取之過程，依序判斷是否為前方車輛。本文將各種天候之車輛特徵加入考量，對於多環境有高度的適應性，同時成功的克服道路標字、強烈光線與陰影、路面反光、路面積水，甚至雨水及雨刷影響等。並且完成無分日夜之車輛偵測，對於緩慢光線變化感快速光線變化下均能夠偵測。進一步使用既有車道線之模型，建立兩旁車道並偵測兩側車輛完成前方多車輛追蹤，同時也推算前方車輛距離以警示駕駛者。

In This thesis the technique of image processing and computer vision theorem are applied to lane detection and vehicle detection. In the meantime, the algorithm is also applied on TI-DM642 for driving assistance system (DAS). The system has been working in different environment such as expressway, urban area and tunnel. Furthermore the algorithm is such robust to be verified with all weather condition like sunny day, cloudy day, evening, morning, night and rainy day. In the lane detection, CCD camera is used to grab the front view, and then the algorithm detects the lane making to contribute a real lane model. This model is applied to estimate and narrow the searching area in order to increase the accuracy and reduce the computation. The lane detection system has been verified successfully in expressway and urban road. Moreover, the system has been equipped on Taiwan iTS-1 (the first intelligence car of Taiwan) as vision system and combines with control system to accomplish lane keeping and lane change. The vehicle detection uses the lane model to build basic vehicle parameters and sets the ROI from the result of lane detection. Algorithm will select multiple possible objects those have strong vehicle characteristics. After feature extraction, the vehicles will be verified with the classify rules. The thesis considers kinds of the vehicle features in different weathers condition and overcomes lots of influence from environment like the text on the road, the strong shadows and light, and the reflected light from the road surface. Even with rainfall and windscreen wiper, the system works successfully. The detection has no relationship with the day or night, so it has good performance at smooth light changing or sudden light changing. With the lane model from lane detection, the algorithm could establish the multiple lane boundaries and finish the multiple forward car detection. At same time, the distance of the forward vehicle can be calculated, and the systems will warn driver that he is in the dangerous distance.