Will this car change the lane? — Turn signal recognition in the frequency domain

Understanding the intention of other road users is a key requirement for autonomous driving. In this regard, one particularly relevant cue is a flashing turn signal, since it gives an important hint regarding the intended driving direction of another vehicle in the next few seconds. As such, turn signals can be considered as one of the first methods invented for car-to-car communication. In contrast to modern radio-based approaches, turn signals are installed in almost every vehicle. However, only image-based methods are able to detect, recognize and understand those signals. In this paper, we present a new method to recognize turn signals of other vehicles in images. Our approach builds upon a robust vehicle detector and involves three major steps applied to each detected vehicle: light spot detection, feature extraction through FFT-based analysis of the temporal signal behavior at each detected light spot, and AdaBoost classification of the extracted feature set. In our experiments, we use solely virtually-generated data for training and evaluate the proposed approach on a large 30 minute real-world image sequence. Our results indicate competitive performance at real-time speeds.