The decision-making module provides proper driving maneuvers for Automated vehicles (AVs), which is essential to the deployment of AVs. Current research works attempted to tackle this issue by optimizing some specific significant objectives or modeling separate typical maneuvers. However, highly dynamic and complicated traffic flows bring huge challenges to this module. This paper develops a convolutional neural network-based (CNN) deep learning framework to learn proper driving maneuvers from bulks of the collected traffic data. The proposed method firstly covers the dynamic traffic information through constructing virtual traffic images, namely dynamic motion images. Those images contain important motion relationships between the ego car and its neighbors. Then, the proposed CNN learning model is trained to capture inherent features from those motion images and to learn to make proper driving behaviors. We employ the learning framework to learn typical highway driving scenarios. Training and test results validate the effectiveness of our proposed CNN-based learning decision-making approach and suggest that deep-learning-based methods have dramatic potential to promote the intelligent driving capability of AVs and thus accelerate the launch of AVs.