Traffic Light Detection in Autonomous Driving Systems

Vision-only systems are currently very popular for use in autonomous driving systems and advanced driver-assistance systems. These systems operate by using input images from a camera and no other sensors. One of the tasks these systems needs to perform is the detection and understanding of traffic lights in a traffic environment, by localizing all relevant traffic lights in an image received from an on board camera mounted on the vehicle. This article proposes a traffic light recognition system where adaptive thresholding and deep learning are used for region proposal and traffic light localization, respectively. The LISA open-source dataset is used along with custom augmentation methods in order to increase the number of available data samples. Performance of the developed system is presented in the form of true and false positive rates obtained on the test data. The classification part of the algorithm gives a total of 89.60% true detection rate, while the regression part of the model produced a correct location of the traffic light in 92.67% of cases.