Human-like Attention-Driven Saliency Object Estimation in Dynamic Driving Scenes

Identifying a notable object and predicting its importance in front of a vehicle are crucial for automated systems’ risk assessment and decision making. However, current research has rarely exploited the driver’s attentional characteristics. In this study, we propose an attention-driven saliency object estimation (SOE) method that uses the attention intensity of the driver as a criterion for determining the salience and importance of objects. First, we design a driver attention prediction (DAP) network with a 2D-3D mixed convolution encoder–decoder structure. Second, we fuse the DAP network with faster R-CNN and YOLOv4 at the feature level and name them SOE-F and SOE-Y, respectively, using a shared-bottom multi-task learning (MTL) architecture. By transferring the spatial features onto the time axis, we are able to eliminate the drawback of the bottom features being extracted repeatedly and achieve a uniform image-video input in SOE-F and SOE-Y. Finally, the parameters in SOE-F and SOE-Y are classified into two categories, domain invariant and domain adaptive, and then the domain-adaptive parameters are trained and optimized. The experimental results on the DADA-2000 dataset demonstrate that the proposed method outperforms the state-of-the-art methods in several evaluation metrics and can more accurately predict driver attention. In addition, driven by a human-like attention mechanism, SOE-F and SOE-Y can identify and detect the salience, category, and location of objects, providing risk assessment and a decision basis for autonomous driving systems.