Intelligent vehicles attempt to improve the daily transportation of people, but their safety has been questioned after numerous trafic accidents. This paper proposes a Safety Driving Framework (SDF) to improve the ability of intelligent vehicles to avoid risks during emergencies. When performing autonomous driving tasks, the SDF interacts with the environment using a camera, makes decisions using convolutional neural networks, and can perform an emergency stop if it encounters an obstacle. In

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... is paper, we examine the potential of attention mechanisms to enhance the performance of convolutional neural networks and construct four convolutional neural networks with attention mechanisms to use in experiments. Additionally, we extend a dataset and enhance the robustness of the model by implementing data augmentation (DA) techniques. We train the model using 10-fold cross-validation. In this article, we build an intelligent driving platform and a simulation track for simulation testing. The experimental results show that CNNs using data reinforcement and with an attention mechanism perform better than existing models. In particular, ENetb0-SE has an average recognition rate of 95.6% for obstacles and an accident rate of 2%, which is much better than existing models.