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Forward safety system (FSS) monitors the roadway ahead and warns the driver when a potential risk exists. The vision-based FSS can provide better protection capability than those based on radars because it owns rich environmental information, such as the shape and color of lane lines and objects. However, the detection of vision-based FSS is generally restricted to a small area in front of the vehicle due to the hardware limitation of the adopted camera. This paper presents a novel FSS which utilizes cameras with different focal lengths to extend the detection range and thus can provide early lane departure and forward collision warning. The proposed FSS extracts lane lines by the intensity and structural properties, and detects potential lane departure and forward collision by analyzing the moving trajectory and by detecting the obstacle ahead, respectively.

This study proposes an anti-counterfeiting system of drunk driving, which prevents drivers from drunken driving and cheat of driver's alcohol detection. The study develops the technology of driver's facial image match by a serial image processes. The methodology of facial image match uses the Adaboost algorithm, Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) to extract the facial features of drivers, is suitable to be applied under the internal environments of cabin. By analyzing the facial features of drivers, the time of driver's exchange is detected, and the driver's identity is indentified. When the cheat act of driver's alcohol detection occurred, the system will generate warning signals through a buzzer to notice the driver to take alcohol detection.