在智慧型運輸系統(ITS)蓬勃的發展下，智慧型車輛技術也是發展的趨勢，而不只是強調安全性的駕駛輔助系統，也越來越多學術界跟產業界注重因乘坐車子產生不適感而研發的技術，我們可以藉由得知前方道路的曲率，而對汽車座椅做適當的角度調整以抵銷離心力。智慧型車輛技術為多方面發展的，而其中車道線偵測是基本且重要的技術。
在本篇論文中，我們提出一個基於機器視覺的即時車道偵測及追蹤系統，並且從我們的道路模型估計前方的車道曲率。利用逆透視轉換(Inverse Perspective Mapping)將影像轉換為真實世界座標，並利用Mean shift跟Hough transform偵測道路線，再用卡爾曼濾波器進行車道線追蹤，經過拋物線擬合之後，我們就可以得到前方道路的曲率估計值。
我們針對連續彎道並在不同的環境下進行實驗，我們提出的方法可以有效的偵測到彎曲道路，在多彎道的郊區環境下能達到99.4%的偵測率，而在有切換車道狀況的高速公路的環境下也能達到94.5%的偵測率；計算前方道路曲率跟實際的曲率誤差值小於0.01，整體運算速度可達每秒23~30幀畫面。

In the vigorous development of Intelligent Transportation Systems (ITS), intelligent vehicle technology is the focus of development. Not just the emphasis on safety driving assistant system, more and more academia and industry attention to the developed technology of the resolution of the discomfort by taking a car. We can through obtaining the curvature of the forward lane, while the angle of the car seat to make the appropriate adjustments to offset the centrifugal force. Intelligent vehicle technology for a wide range of development, especially for lane detection is a fundamental and important technology.
In this paper, we propose a based on machine vision lane detection and tracking system in real-time. And estimate the curvature of the forward lane from our lane model. The use of Inverse Perspective Mapping (IPM) convert images to the world coordinate system, and using mean shift with the Hough transform to detect lane markings, then the lane tracking with Kalman filter. After the parabola fitting, we can get the curvature of forward lane.
We carry on the experiments for the continuous curve in different environments; our proposed method can effectively detect the curved lane. The detection rate is 99.4% in multi-curves suburban environment, and the detection rate of 94.5% in the state of switch lanes in the freeway environment. The error value is less than 0.01 between our estimated curvature of the forward lane and the actual curvature. The overall performance is 23 to 30 frames per second.

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