ARAGAN: A dRiver Attention estimation model based on conditional Generative Adversarial Network

Predicting driver's attention in complex driving scenarios is becoming a hot topic due to it helps the design of some autonomous driving tasks, optimizing visual scene understanding and contributing knowledge to the decision making. We introduce ARAGAN, a driver attention estimation model based on a conditional Generative Adversarial Network (cGAN). This architecture uses some of the most challenging and novel deep learning techniques to develop this task. It fuses adversarial learning with Multi-Head Attention mechanisms. To the best of our knowledge, this combination has never been applied to predict driver's attention. Adversarial mechanism learns to map an attention image from an RGB traffic image while mapping the loss function. Attention mechanism contributes to the deep learning paradigm finding the most interesting feature maps inside the tensors of the net. In this work, we have adapted this concept to find the saliency areas in a driving scene. An ablation study with different architectures has been carried out, obtained the results in terms of some saliency metrics. Besides, a comparison with other state-of-the-art models has been driven, outperforming results in accuracy and performance, and showing that our proposal is adequate to be used on real-time applications. ARAGAN has been trained in BDDA and tested in BDDA and DADA2000, which are two of the most complex driver attention datasets available for research.