Detection of the traffic light is a key function of the automatic driving system for urban traffic. Considering the characteristics of classical and self-learning algorithms, a fusion logic is proposed to make up the shortcoming of learning algorithms by combining the known knowledge with the learning features to detect the red and yellow–green traffic light without turn indicator. The relationship of detection performance among different detectors is established analytically. Then the improvement of detection performance by fusion is analysed theoretically and optimised numerically. According to the analysis results, the hybrid detector is designed by using the colour information in hue-saturation-intensity to extract the candidate region, the hog feature to identify the shape information of traffic light classified by a support vector machine, and a comparatively simple convolutional neural network (CNN) with the classical AlexNet structure to act as the self-learned detector. The effectiveness of the hybrid method is validated by several comparative tests with single CNN detectors and other fusion methods on the training dataset, and the extensibility to new application conditions is evaluated by vehicle tests.

中文翻译：

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结合经典和自学习探测器的交通信号灯混合策略

交通信号灯的检测是城市交通自动驾驶系统的关键功能。考虑到经典算法和自学习算法的特点，提出了一种融合逻辑来弥补学习算法的缺陷，将已知知识与学习特征相结合来检测没有转向指示器的红色和黄绿色交通信号灯。通过分析建立了不同检测器之间检测性能之间的关系。然后对融合检测的性能进行了理论分析和数值优化。根据分析结果，通过利用色相饱和度强度中的颜色信息提取候选区域，通过猪的特征来识别支持向量机分类的交通信号灯的形状信息，来设计混合检测器，以及具有简单AlexNet结构的相对简单的卷积神经网络（CNN）充当自学习检测器。混合方法的有效性通过在训练数据集上使用单个CNN检测器进行的几次比较测试和其他融合方法验证，并且通过车辆测试评估了对新应用条件的可扩展性。