Steering a car through traffic is a complex task that is difficult to cast
into algorithms. Therefore, researchers turn to training artificial neural
networks from front-facing camera data stream along with the associated
steering angles. Nevertheless, most existing solutions consider only the visual
camera frames as input, thus ignoring the temporal relationship between frames.
In this work, we propose a Convolutional Long Short-Term Memory Recurrent
Neural Network (C-LSTM), that is end-to-end trainable, to learn both visual and
dynamic temporal dependencies of driving. Additionally, We introduce posing the
steering angle regression problem as classification while imposing a spatial
relationship between the output layer neurons. Such method is based on learning
a sinusoidal function that encodes steering angles. To train and validate our
proposed methods, we used the publicly available Comma.ai dataset. Our solution
improved steering root mean square error by 35% over recent methods, and led to
a more stable steering by 87%.

该研究提出了一种基于 C-LSTM 的全面可训练的人工神经网络模型，用于根据前置摄像头数据流和关联的车辆转向角度学习驾驶的视觉和动态时间依赖关系。同时，将转向角度回归问题作为分类问题，并在输出层神经元之间施加一定的空间关系，从而通过学习编码转向角度的正弦函数的方法来进行转向预测，该方法在公开数据集 Comma.ai 上验证，相较于现有方法，平均根均方误差提升了 35％，转向更为稳定。