Vehicle taillight recognition is an important application for automated driving, especially for intent prediction of ado vehicles and trajectory planning of the ego vehicle. In this work, we propose an end-to-end deep learning framework to recognize taillights, i.e. rear turn and brake signals, from a sequence of images. The proposed method starts with a Convolutional Neural Network (CNN) to extract spatial features, and then applies a Long Short-Term Memory network (LSTM) to learn temporal dependencies. Furthermore, we integrate attention models in both spatial and temporal domains, where the attention models learn to selectively focus on both spatial and temporal features. Our method is able to outperform the state of the art in terms of accuracy on the UC Merced Vehicle Rear Signal Dataset, demonstrating the effectiveness of attention models for vehicle taillight recognition.
翻译:车辆尾灯识别是自动驾驶的一个重要应用,特别是用于对Ado飞行器的意向预测和自我飞行器的轨迹规划。在这项工作中,我们提议了一个端到端深学习框架,以识别从图像序列中产生的尾灯,即后转和刹车信号。拟议方法从一个革命神经网络(CNN)开始,以提取空间特征,然后应用一个长期短期内存网络(LSTM)学习时间依赖性。此外,我们把注意模型纳入空间和时间领域,在空间和时间领域,关注模型学会有选择地侧重于空间和时间特征。我们的方法能够从UC Merced 机动车辆再行信号数据集的准确性方面超越艺术状态,表明车辆尾灯识别的注意模型的有效性。