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

arXiv: https://arxiv.org/abs/2111.00087 · HTML: https://mlsanity.com/paper/2111.00087/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Oct 2021.

## Built on by (most cited first, 2 of 2)

- [Considering Human Factors in Risk Maps for Robust and Foresighted Driver Warning](https://mlsanity.com/paper/2306.03849/) (2023)
- [Effects of Augmented-Reality-Based Assisting Interfaces on Drivers' Object-wise Situational Awareness in Highly Autonomous Vehicles](https://mlsanity.com/paper/2206.02332/) (2022)

## Abstract (arXiv)

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.