# Traffic Light and Arrow Signal Recognition Based on a Unified Network
## 1 Introduction  
Advanced driver assistance systems currently installed in vehicles, such as autonomous driving vehicles, have achieved favorable results. These systems greatly reduce the necessity of driver control in locations with invariable landscapes, such as highways and enclosed parks. They are also capable of planning routes and navigating vehicles to destinations while helping them to avoid obstacles. However, for self-driving technology to be more widely applicable, additional landscapes (e.g., city streets) must be incorporated into these systems. Specifically, self-driving technology must be able to detect traffic conditions and determine whether to continue driving or stop according to traffic signals. Traffic light detection techniques are typically categorized into two classes. One class involves the detection of traffic lights and communicating with neighboring vehicles through vehicle-to-infrastructure (V2I) communications [1]. The second class involves the detection of traffic light positions and states by using vehicles' sensors. The first class is usually more expensive to implement than the second class.  
Traffic light recognition methods that use vehicle onboard sensors have been extensively studied [2]. In several methods, advanced image processing techniques are primarily applied to the sequence images captured by in-vehicle cameras. Learning-based methods have become increasingly popular because of their excellent classification performance [3; 4; 5]. However, detection accuracy remains unsatisfactory due to the presence of multiple disturbance factors in outdoor environments, such as incomplete light shapes, dark light states, and partial occlusion. These issues are troublesome to overcome with computer vision and image processing techniques. However, the research on convolutional neural networks (CNN) for traffic light detection [6; 7] has contributed to the development of learning-based methods with effective feature extraction for classification.  
Conventional image-based traffic light detection methods can misjudge traffic situations when background features that resemble traffic lights are detected. Traffic lightdetection requires high accuracy because it affects subsequent vehicle control decisions. Accordingly, map-based detection methods have been proposed as a supplement to image-based detection methods, where the aim is to reduce misjudgment and enhance accuracy.  
In this paper, we propose a traffic light recognition approach for traffic lights using deep neural networks. Our approach focuses on the detection of arrow signal lights. For traffic light detection, we use map data to facilitate detection by restricting the region of interest (ROI). We use two cameras with different focal lengths to capture nearby and faraway scenes. For recognition, we propose a technique that combines object detection and classification. In addition, we propose a unified network by sharing features to decrease training and computation. The results demonstrate the effectiveness of the proposed method in detecting traffic lights.  
The contributions of the proposed approach are as follows: (i) using map information and two various focal length cameras for traffic light detection at different distances, (ii) proposing a technique that combines object detection and classification to solve the issue of multiple light state classes, and (iii) integrating the network for detecting traffic lights to a unified network by sharing feature maps for efficiency.