Traffic light detection is essential for self-driving cars to navigate safely
in urban areas. Publicly available traffic light datasets are inadequate for
the development of algorithms for detecting distant traffic lights that provide
important navigation information. We introduce a novel benchmark traffic light
dataset captured using a synchronized pair of narrow-angle and wide-angle
cameras covering urban and semi-urban roads. We provide 1032 images for
training and 813 synchronized image pairs for testing. Additionally, we provide
synchronized video pairs for qualitative analysis. The dataset includes images
of resolution 1920$\times$1080 covering 10 different classes. Furthermore, we
propose a post-processing algorithm for combining outputs from the two cameras.
Results show that our technique can strike a balance between speed and
accuracy, compared to the conventional approach of using a single camera frame.

该研究提出了一种新颖的基准交通灯数据集，包括用于训练和测试的图像和视频，还提出了一种使用双摄像头输出的后处理算法，可以在速度和准确性之间取得平衡，以便自动驾驶汽车可以安全地在城市和半城市道路上导航。