本篇論文提出偵測多條車道線及多台車輛的方法。傳統上，偵測車道線及車輛的方式是互相獨立的，但是相反地本篇論文利用兩者的 偵測結果互相輔助的方式，整合這兩種偵測方式來得到更精準的結果。當利用某些特徵在尋找車道線時，經常會受到圖形中車輛的邊緣及顏色所影響；此外，因為有些其他物體的特徵相似於車輛，所以也會被誤判為車輛，導致車輛偵測的結果不夠準確。本論文中利用找出車道中心點的位置到可能是車輛的目標物之間的距離，利用此距離之性質，過濾掉某些不是車輛的物體。在偵測車道線方面，則利用車道線的方向與車輛的移動方向之間的關係來判別車道線的可能解。本論文利用迭代優化演算法（Iterative Optimization Algorithm）的估算，可以得到接近理想車道線及車輛的結果。實驗結果中，其偵測錯誤率由 32.6%降到 2.7%。

This paper presents an approach to detect multiple lane and vehicles. Instead of assuming that the processes of lane and vehicle detection are independently, we integrate these two processes in a mutually supporting way to achieve more accurate results. In lane boundary detection, the features of lane boundary often affect by the edge and color of the vehicle. Furthermore, the results of vehicle detection could be non-robust if there are some non-vehicle objects that have similar features to vehicle. Here, we use the distance of the position between central position of lane boundary and vehicle position from hypotheses to filter out the non-vehicle object. And we use the similarity of the lane boundaries direction and the moving direction from hypotheses to get the optimal lane solution. By applying iterative optimization algorithm, we can achieve sub-optimal solution of lane and vehicle detection and the experimental results shows that the error rate is success-fully reduced from 32.6% to 2.7%.