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Vehicle taillight recognition is an important application for automateddriving, especially for intent prediction of ado vehicles and trajectoryplanning of the ego vehicle. In this work, we propose an end-to-end deeplearning framework to recognize taillights, i.e. rear turn and brake signals,from a sequence of images. The proposed method starts with a ConvolutionalNeural Network (CNN) to extract spatial features, and then applies a LongShort-Term Memory network (LSTM) to learn temporal dependencies. Furthermore,we integrate attention models in both spatial and temporal domains, where theattention models learn to selectively focus on both spatial and temporalfeatures. Our method is able to outperform the state of the art in terms ofaccuracy on the UC Merced Vehicle Rear Signal Dataset, demonstrating theeffectiveness of attention models for vehicle taillight recognition.

An Attention-based Recurrent Convolutional Network for Vehicle Taillight Recognition