Improving Driver Situation Awareness Prediction using Human Visual Sensory and Memory Mechanism
This work addresses safety in autonomous driving by improving warning systems for drivers, but it is incremental as it builds on existing gaze-based models.
The paper tackled predicting driver situation awareness by incorporating object properties and human visual sensory and memory mechanisms, achieving over 70% accuracy and outperforming baselines.
Situation awareness (SA) is generally considered as the perception, understanding, and projection of objects' properties and positions. We believe if the system can sense drivers' SA, it can appropriately provide warnings for objects that drivers are not aware of. To investigate drivers' awareness, in this study, a human-subject experiment of driving simulation was conducted for data collection. While a previous predictive model for drivers' situation awareness utilized drivers' gaze movement only, this work utilizes object properties, characteristics of human visual sensory and memory mechanism. As a result, the proposed driver SA prediction model achieves over 70% accuracy and outperforms the baselines.