視覺式駕駛安全輔助系統相關技術在距今約二十多年前開始被重視與開發，透過攝影機以視覺式的方式分析車輛前方道路的狀況來輔助駕駛者。其中針對汽車之視覺式駕駛安全輔助系統近年來已逐漸完善，反觀機車之視覺式駕駛安全輔助系統並未被重視。機車以及汽車數量逐年提高，而每年機車上升的數量較汽車多了約五萬。上述情況最終導致汽車交通肇事率逐年降低，而機車交通肇事率逐年上升的問題。
前方車輛偵測技術於白天場景已逐漸成熟，但是較少研究者針對夜晚場景進行開發與研究。透過近年來夜晚前方車輛偵測技術文獻可知，許多研究會藉由車尾燈偵測之相關技術，確認車輛位置。因此本研究將利用車尾燈偵測確認車輛位置，而由於本研究需進行前方車輛減速偵測，故本研究將針對車尾燈區域之剎車燈啟動與否判斷前方車輛是否減速。
由於機車有轉彎的情況，因此本研究將進行Region of Interest (ROI) 範圍調整。當車輛遇紅色交通號誌停止移動時，因不會與前方車輛發生交通事故，所以不需進行車尾燈偵測以及剎車燈啟動偵測，故本研究需偵測前方車輛是否移動。由於近年來車輛之車尾燈並不一定為傳統圓形形狀之車尾燈，還有不規則形狀之車尾燈以及長條形狀之車尾燈，因此本研究將針對車尾燈周圍環繞光源的特性進行車尾燈偵測。本研究於剎車燈啟動偵測中將利用其亮度以及門檻值判斷是否啟動，而此門檻值為動態形式，將根據車尾燈至攝影機之距離的不同決定其門檻值。由於某些剎車燈啟動時其亮度值低於本研究決定之門檻值以及某些剎車燈未啟動時其亮度值高於本研究決定之門檻值，將導致剎車燈啟動偵測失敗。因此本研究將針對此類車尾燈個別調整其門檻值，以提高剎車燈啟動偵測之正確率。
最後實驗的部分，本研究針對晴天、雨天以及隧道三種場景以及數種道路種類進行實驗。實驗結果呈現出，本研究在不考慮地面紅色反光時車尾燈偵測結果皆能產生較高的正確率，而地面紅色反光期望於未來能進行過濾，以提升車尾燈偵測正確率。本研究於剎車燈啟動偵測中若不考慮雨天時因雨滴滴落鏡頭上的情況，則剎車燈啟動偵測之正確率約略為90%。

Vision-based driver assistance systems and its related technologies were started to pay attention and develop from about 20 years ago. Visual analyzing the road situation in front of vehicles through camera to assist drivers. Vision-based driver assistance systems for automobile has been gradually consummated. In contrast, vision-based driver assistance systems for motorcycle went unheeded. The quantity of motorcycle and automobile increases year by year, and the quantity of motorcycle is fifty thousand more than automobile per year. Summarizes the above situation causes that automobile traffic accident rate reduces year after year, but motorcycle traffic accident rate rises every year.
Daytime forward vehicle detection technology has been matured by degrees, but there is not so much researchers developing and researching at nighttime. By literatures in recent years of nighttime forward vehicle detection technology, many researches confirm the location of vehicle through related technologies about taillight detection. Therefore this study will use taillight detection to confirm the location of vehicle. Because it has to do a forward vehicle deceleration detection, forward vehicle decelerates or not will be determined by the brake-lights activates or not.
When the motorcycle turns a corner, this study will adjust Region of Interest (ROI). There will not be traffic accidents with the forward vehicle when the vehicle stop moving as the red traffic light shows. So it hasn’t to do a taillight detection and brake-light detection. Therefore our system needs to detect forward vehicle move or not. The shape of taillight in the recent years is not only traditional circle but also irregular shape or elongated shape, and therefore this study will aim at the characteristic of surrounding light source around the taillight to do a taillight detection.
This study will use illumination and threshold to determine brake-light on or off, and this dynamic threshold according to the distance between taillight and camera. The illumination of some activated brake-lights is lower than our determined threshold, and some non-brake of taillights are higher than it. It will lead to failure of brake-light detection. So our system will adjust threshold specifically to increase the accuracy rate of brake-light detection.
Our system experiments on sunny day, rainy day, in the tunnel, and on many kinds of roads. The experiment result shows that it will get the higher accuracy rate without considering the consequence of taillight detection with the reflection of red lights. And our system expects that the reflection of red lights can be filter in the future to increase the accuracy rate of taillight detection. In this study, if it doesn’t consider raindrop dripping on the camera lens on rainy day, the accuracy rate of brake-light detection is about 90%

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