整合車道幾何與車流方向資訊之電腦視覺駕駛輔助系統

Vision-Based Driver Assistance System using Integration Information from Lane Geometry and Traffic Direction

黃贊宇(Chan-Yu Huang)

指導教授 ： 傅立成

共同指導教授 ： 蕭培墉

國立臺灣大學/電機資訊學院/資訊工程學研究所/碩士(2007年)

https://doi.org/10.6342/NTU.2007.01269

摘要

本論文提出一個偵測車輛前方多車道線以及多車輛的方法。我們假設車道線偵測以及車輛偵測會各自獨尋找影像中的車道以及車輛，透過整合車道線偵測及車輛偵測的結果資訊就可取得更為準確的偵測結果。
在車道線偵測方面，車道線特徵的分析常受到前方車輛的邊線或是車輛上的顏色所影響，導致特徵分析錯誤。同樣的在車輛偵測方面，出現在背景中與車輛相似的特徵會干擾車輛特徵之分析，導致車輛偵測不穩定的發生。
因此，本論文中使用車輛假設之位置與車道中心之間的距離，過濾不可能為車輛的物件。另一方面，使用車輛移動方向與車道線之方向的相似程度，取得最佳的車道線偵測結果。為了整合此些資訊，我們使用反覆執行之最佳化演算法將資料整合並取得最佳的結果，並利用車道線偵測以及車輛偵測之方法取得近似的結果，取得整合所需的資訊。最後在實驗結果的驗證中，我們與一般的偵測方法做比較並驗證出本論文方法之效果。

關鍵字

多車輛偵測 ； 多車道線偵測 ； 資訊整合平台 ； 曲線車道線偵測

並列摘要

This thesis presents an approach to detect multiple lane and vehicles. Instead of assuming that the processes of lane and vehicle detection should do independently, we integrate these two processes in a mutually supporting way to achieve more accurate results.
In lane boundary detection, the process of identifying possible features of a lane boundary is often affected by the edges and color of the vehicles on the road. Likewise, the results of vehicle detection could be non-robust if there are some background features which can confuse the process of indentifying possible vehicle features.
Thus, in the thesis, we use the distance between the central position of a lane and the position of the hypothesized vehicle to filter out the non-vehicle object. And we use the similarity between the lane boundary direction and the moving directions of the hypothesized vehicles to get the optimal lane solution. By applying iterative optimization algorithm, we can obtain the near-optimal solutions of both lane and vehicle detections. Finally the experimental results are provided to validate the effectiveness of the proposed novel approach.

並列關鍵字

multiple vehicle detection ； multiple lane detection ； integratio framework ； curve lane detection

參考文獻

[3]Y. Wang, E. K. Teoh, and D. Shen, "Lane detection and tracking using B-Snake," Image and Vision Computing, vol. 22, pp. 269-280, 2004.

[4]S.-S. Huang, C.-J. Chen, P.-Y. Hsiao, and L.-C. Fu, "On-Board Vision System for lane Recognition and Front-Vehicle Detection to Enhance Driver's Awareness," in IEEE International Conference on Robotics and Automation, New Orleans, U.S.A., 2004, pp. 2456-2461.

[5]M. Bertozzi and A. Broggi, "GOLD: A parallel real-time stereo vision system for generic obstacle and lane detection," IEEE Transactions on Image Processing, vol. 7, January 1998.

[6]K.-Y. Chiu and S.-F. Lin, "Lane Detection using Color-Based Segmentation," in IEEE Intelligent Vehicles Symposium, 2005.

[8]R. Labayrade, J. Douret, J. Laneurit, and R. Chapuis, "A reliable and robust lane detection system based on the parallel use of three algorithms for driving safety assistance," Ieice Transactions on Information and Systems, vol. E89D, pp. 2092-2100, Jul 2006.