Enhancing autonomous driving systems with deep learning and spatial channel attention mechanisms: an experimental study

With the advancement of technology, autonomous driving has become a popular research field. However, designing an autonomous driving system that can handle complex environments and meet real-time requirements remains an open challenge. In this study, we explore how to implement an efficient and reliable autonomous driving system using deep learning techniques. We experimented with a series of deep learning models, including VGG16, Inception series models, ResNet, MobileNetV2 and DenseNet121. By comparing the performance of these models on our dataset, we identified the best performing ones and fine-tuned them to optimize their performance for our task. In addition, to further improve the performance of the model, we introduced a spatial channel attention mechanism. We found that deploying the spatial channel attention mechanism at appropriate locations could significantly improve the performance of the model. However, deploying the spatial channel attention mechanism at all locations might cause the model to focus too much on local features and ignore global features. Overall, our results show that the performance of autonomous driving systems can be effectively improved by improving the network structure and training methods.