Improving Driver Situation Awareness Prediction using Human Visual Sensory and Memory Mechanism

TL;DR

This paper introduces a novel driver situation awareness prediction model that incorporates human visual sensory and memory mechanisms, achieving over 70% accuracy and surpassing previous gaze-based models.

Contribution

It presents a new predictive model for driver SA that integrates object properties and cognitive mechanisms, improving accuracy over gaze-only models.

Findings

Achieved over 70% prediction accuracy.

Outperformed baseline gaze-based models.

Validated through human-subject driving simulation experiments.

Abstract

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.