Deep Traffic Light Perception with Spatiotemporal Analysis for Autonomous Driving

Traffic light perception is crucial for autonomous driving. In this paper, a three-step method for traffic-light detection, recognition and understanding is developed to guide self-driving vehicles to pass crossroads. The first step adopts a state-of-the-art deep neural object detection architecture to detect traffic lights. The second step designs a novel four-channel convolutional neural network to classify traffic lights. The last step develops a spatiotemporal trajectory analysis method to filter out false positives and to guide self-driving vehicles. The proposed method is evaluated on two datasets, and experiment results show that it can efficiently perceive traffic-light states and perform better than baseline considered. Furthermore, real vehicle testings are conducted, which demonstrate the effectiveness of the proposed method.