進階駕駛輔助系統之目的主要是提供車輛周圍環境與行車狀況相關資訊給駕駛人，以降低交通意外發生。車輛行駛常見的危險包括前方車輛突然煞車或者變換車道，導致與行駛車輛距離過近造成追撞事故，為了防止碰撞事故的發生，因此本篇論文提出一套『全天候即時前車偵測與信號辨識系統』，透過行車紀錄器的影像加以處理，將前方車輛提取出來，並辨識車輛的信號，例如煞車事件與轉向事件，顯示於行車紀錄器畫面上，供駕駛人參考並給予提醒，以提升行車安全。

本篇論文提出之系統分為六大部分: (1)環境亮度辨識、(2)車輛候選區域提取、(3)車輛驗證、(4)車輛追蹤、(5)車輛尾燈偵測，以及(6)信號辨識。車輛候選區域是以不同環境亮度的車輛特徵作為偵測；之後進行車輛驗證，我們以局部二元模式作為特徵，並以自適應增強演算法訓練出分類器，驗證候選區域是否為車輛；再下一步驟搭配物件追蹤演算法對車輛進行追蹤，使車輛偵測更穩定，以利車輛信號的判定；車輛信號辨識是以車燈的歷史資訊轉換成頻率特徵訓練出分類器，檢測車燈是否為閃爍狀態進而辨識車輛的轉向事件，並且偵測車輛第三煞車燈作為判斷車輛的煞車事件。

實驗結果可以看出我們的系統可以達到即時的運算處理，並且可適應於各種天候場景，同時車輛偵測率與信號辨識率分別達到93.1%與89.4%，顯示出本系統的穩健性。

Advanced driver assist system is a technology that provides drivers with essential information when driving and lead to avoid traffic accidents. Forward vehicle braking or changing lane out of a sudden are the most common dangers which lead to collision accidents. To prevent collision accident, we propose “a real-time forward vehicle detection and signal recognition system in all-weather situations”. We extract forward vehicle by images of dashboard camera, identify signals of vehicle such as braking and turning, and then display them on screen of dashboard camera to notify driver for safety.

In this thesis, our system consists of six main parts: (1) Lighting condition recognition, (2) Vehicle candidate extraction, (3) Vehicle verification, (4) Vehicle tracking, (5) Taillight detection and (6) Signal recognition. Vehicle candidate is detected by vehicle characteristics in different illumination environments. In vehicle verification, we use local binary pattern as feature, training our classifier to verify the vehicle with AdaBoost algorithm. To make signal recognition stable we use object tracking algorithm to support vehicle detection. In signal recognition, we record history information of vehicle lights and use its flicking frequency as feature to train classifier, identify vehicle turning by checking whether lights are flickering or not, and then detect third brake light of vehicle to confirm braking.

The experimental results show that our system is real-time, and is works in various weather condition. The accuracy of vehicle detection and signal recognition rates achieve 93.1% and 89.4%, which show the stability of our system.

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