Multisource Adaption for Driver Attention Prediction in Arbitrary Driving Scenes

Abstract

Driver attention cues contribute to the following intended maneuver prediction and provide a risk indicator for the advanced driver-assistance systems in complex driving scenarios. The diverse traffic scenes result in a challenging task to predict human visual attention with high generalization capability. The data heterogeneity caused by multiple sources with different data characteristics, such as video sources, was investigated in the current research and mitigated using the domain adaption modules (i.e., domain-specific batch normalization, Gaussian priors, and smoothing filter) and the domain-specific focal loss. Inspired by human attention mechanism, generic coders and task-driven attention modules were incorporated into a lightweight network to replicate human-like perceptual patterns, such as the perception of latent risk. Integrating these adaptive modules, we proposed the adaptive driver attention (ADA) model to predict salient regions in different traffic scenes. Consequently, the ADA model trained jointly on four driver attention datasets achieves the best performance against the state-of-the-art methods across seven metrics. Retrospective visualizations of the network and cross-validation results further explain the merit of the proposed method. Since these approaches are generic, adaptive modules are universally applicable for standard deep neural network architectures to alleviate the heterogeneity across datasets and easily extended for arbitrary traffic scenes in the real world.