Attention-based recurrent convolutional network for vehicle taillight recognition

Abstract

A method for performing vehicle taillight recognition is described. The method includes extracting spatial features from a sequence of images of a real-world traffic scene during operation of an ego vehicle. The method includes selectively focusing a convolutional neural network (CNN) of a CNN-long short-term memory (CNN-LSTM) framework on a selected region of the sequence of images according to a spatial attention model for a vehicle taillight recognition task. The method includes selecting, by an LSTM network of the CNN-LSTM framework, frames within the selected region of the sequence of images according to a temporal attention model for the vehicle taillight recognition task. The method includes inferring, according to the selected frames within the selected region of the sequence of images, an intent of an ado vehicle according to a taillight state. The method includes planning a trajectory of the ego vehicle from the intent inferred from the ado vehicle.