Driver visual attention model based on top-down mechanism

As a result of driver’s visual fixation behaviours, experienced drivers are able to selectively focus on specific areas or objects within the scene, ensuring the safe performance of driving tasks. Therefore, modeling driver’s visual fixation behaviours is crucial for the development of autonomous driving systems (ADS). Research indicates that driver’s visual fixation is determined by both top-down and bottom-up mechanisms. This paper proposes a driver’s visual attention model that incorporates both top-down and bottom-up mechanisms. We consider expectancy, effort, and value as top-down factors and salience as a bottom-up factor. The DFF model has been developed with the objective of integrating the top-down factors, while existing models are used to represent the bottom-up factors. Subsequently, a fusion strategy is employed to integrate all features, thereby generating a driver’s visual attention map. The proposed model was trained with the DR(eye)VE dataset and subsequently compared with state-of-the-art (SOTA) models. The results demonstrate that the proposed model exhibits enhanced performance in terms of the Pearson correlation coefficient (CC) and similarity (SMI) metrics, with an improvement of 22.4% and 26.7%, respectively. Furthermore, the trained model was tested on the BDDA dataset to assess its cross-dataset generalisation capability.