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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 successfully reduced from 32.6% to 2.7%.