Traffic light detection and recognition for autonomous vehicles

• Published in: Journal of China universities of posts and telecommunications Vol. 22; no. 1; pp. 50 - 56
• Main Authors: Mu, Guo, Xinyu, Zhang, Deyi, Li, Tianlei, Zhang, Lifeng, An
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.02.2015
• Subjects: Algorithms; Autonomous; autonomous vehicle; Color; Feature recognition; histogram of oriented gradients; Recognition; Support vector machines; traffic light detection and recognition; Traffic signals; Vehicles; 交通信号灯; 交通灯; 城市环境; 支持向量机; 检测; 自动驾驶汽车; 识别算法; 高精度; traffic light detection and recognition; autonomous vehicle; histogram of oriented gradients
• ISSN: 1005-8885
• DOI: 10.1016/S1005-8885(15)60624-0

Traffic light detection and recognition is essential for autonomous driving in urban environments. A camera based algorithm for real-time robust traffic light detection and recognition was proposed, and especially designed for autonomous vehicles. Although the current reliable traffic light recognition algorithms operate well under way, most of them are mainly designed for detection at a fixed position and the effect on autonomous vehicles under real-world conditions is still limited. Some methods achieve high accuracy on autonomous vehicle, but they can＇t work normally without the aid of high-precision priori map. The authors presented a camera-based algorithm for the problem. The image processing flow can be divided into three steps, including pre-processing, detection and recognition. Firstly, red-green-blue （RGB） color space is converted to hue-saturation-value （HSV） as main content of pre-processing. In detection step, the transcendental color threshold method is used for initial filterings, meanwhile, the prior knowledge is performed to scan the scene in order to quickly establish candidate regions. For recognition, this article use histogram of oriented gradients （HOG） features and support vector machine （SVM） as well to recognize the state of traffic light. The proposed system on our autonomous vehicle was evaluated. With voting schemes, the proposed can provide a sufficient accuracy for autonomous vehicles in urban enviroment.