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This paper proposes a method of detecting driving vehicles, estimating the distance, and detecting whether the brake lights of the detected vehicles are turned on or not to prevent vehicle collision accidents in highway tunnels. In general, it is difficult to determine whether the front vehicle brake lights are turned on due to various lights installed in a highway tunnel, reflections on the surface of vehicles, movement of high-speed vehicles, and air pollution. Since driving vehicles turn on headlights in highway tunnels, it is difficult to detect whether the vehicle brake lights are on or not through color and brightness change analysis in the brake light area only with a single image. Therefore, there is a need for a method of detecting whether the vehicle brake lights are turned on by using a sustainable change obtained from image sequences and estimated distance information. In the proposed method, a deep convolutional neural network(DCNN) is used to detect vehicles, and inverse perspective mapping is used to estimate the distance. Then, a long short-term memory (LSTM) Network that can analyze temporal continuity information is used to detect whether the brake lights of the detected vehicles are turned on. The proposed method detects whether or not the vehicle brake lights are turned on by learning the long-term dependence of the detected vehicles and the estimated distances in an image sequence. Experiments on the proposed method in highway tunnels show that the detection accuracy of whether the front vehicle brake lights are turned on or not is 90.6%, and collision accidents between vehicles can be prevented in highway tunnels.

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