Convolutional neural network based vehicle turn signal recognition

This Automated driving is an emerging technology in which a car performs recognition, decision making, and control. Recognizing surrounding vehicles is a key technology in order to generate a trajectory of ego vehicle. This paper is focused on detecting a turn signal information as one of the driver's intention for surrounding vehicles. Such information helps to predict their behavior in advance especially about lane change and turn left-or-right on intersection. Using their intension, the automated vehicle is able to generate the safety trajectory before they begin to change their behavior. The proposed method recognizes the turn signal for target vehicle based on mono-camera. It detects lighting state using Convolutional Neural Network, and then calculates a flashing frequency using Fast Fourier Transform.