In this paper, we apply the attention mechanism to autonomous driving for steering angle prediction. We propose the first model, applying the recently introduced sparse attention mechanism to visual domain, as well as the aggregated extension for this model. We show the improvement of the proposed method, comparing to no attention as well as to different types of attention.
@cite_19: We propose sparsemax, a new activation function similar to the traditional softmax, but able to output sparse probabilities. After deriving its properties, we show how its Jacobian can be efficiently computed, enabling its use in a network trained with backpropagation. Then, we propose a new smooth and convex loss function which is the sparsemax analogue of the logistic loss. We reveal an unexpected connection between this new loss and the Huber classification loss. We obtain promising empirical results in multi-label classification problems and in attention-based neural networks for natural language inference. For the latter, we achieve a similar performance as the traditional softmax, but with a selective, more compact, attention focus.
Much less work has been done on the application of attention mechanism in autonomous driving. In this article: i) we propose a new sparse attention model, based on the sparsemax function @cite_19 , yields better performance; ii) we demonstrate that bagging multiple sparse attention models can provide a significant performance improvement over single models; iii) we show that the proposed architecture performs better than the state-of-the-art soft attention model, CNN, CNN+LSTM for steering angle prediction.