Quantitative Evaluation of driver's situation awareness in virtual driving through Eye tracking analysis
Yunxiang Jiang, Qing Xu, Kai Zhen, Yu Chen
TL;DR
This work tackles the challenge of non-intrusively quantifying driver situation awareness (SA) during driving by leveraging eye-tracking data in a virtual reality (VR) setting. It defines three SA levels—$SA_{L1}$ (perception), $SA_{L2}$ (comprehension), and $SA_{L3}$ (projection)—and an overall score $SA_{overall}$, with $SA_{L2}$ using Gaussian weighting and $SA_{L3}$ using mutual information and PCA for aggregation. In a VR experiment with 14 participants across 56 trials, all four SA scores show significant correlations with driving performance, especially $SA_{L3}$ and $SA_{overall}$, while traditional eye-tracking metrics do not. The results demonstrate a non-intrusive, objective proxy for driving performance and SA, with potential for real-time SA assessment in human–machine collaboration settings.
Abstract
In driving tasks, the driver's situation awareness of the surrounding scenario is crucial for safety driving. However, current methods of measuring situation awareness mostly rely on subjective questionnaires, which interrupt tasks and lack non-intrusive quantification. To address this issue, our study utilizes objective gaze motion data to provide an interference-free quantification method for situation awareness. Three quantitative scores are proposed to represent three different levels of awareness: perception, comprehension, and projection, and an overall score of situation awareness is also proposed based on above three scores. To validate our findings, we conducted experiments where subjects performed driving tasks in a virtual reality simulated environment. All the four proposed situation awareness scores have clearly shown a significant correlation with driving performance. The proposed not only illuminates a new path for understanding and evaluating the situation awareness but also offers a satisfying proxy for driving performance.