Exploring the impact of display types of information about autonomous driving in semi-autonomous vehicles on drivers’ situation awareness and take-over performance under different driving scenarios - Chengmin Zhou; Yuxuan Luo; Jake Kaner (PLoS ONE, 2025)
With the advent of the era of autonomous driving, designing an effective and appropriate autonomous driving information display is crucial for ensuring driving safety. Head-up Display (HUD) is regarded as a promising way for presenting in-vehicle information in the future. This study conducted a simulation experiment to explore the impacts of three types of autonomous driving information displays on HUD on Situation Awareness (SA) and take-over performance, while considering the complexity of different driving scenarios. The experiment used in this study adopted a Latin square experimental design and employed an integrated eye-tracking technology with self-reporting and the Situation Awareness Global Assessment Technique (SAGAT). The results show that although young drivers perform better with the Augmented Reality (AR) display in various complex scenarios, particularly in high-complexity scenarios (the fixation duration with AR display was significantly shorter than that with Pseudo-3D (P3D) display; P = 0.012). However, the advantages of the AR display will weaken as the complexity of the scenarios decreases. Additionally, the Surround Recognition (SR) display is more likely to reduce drivers’ SA (the fixation counts on the SR display was significantly higher than that on the P3D and AR displays; P < 0.001) and take-over efficiency (the take-over reaction time for the SR display was significantly longer than that for the AR display; P = 0.09), especially in medium-complexity scenarios. Meanwhile, male participants pay more attention to the autonomous driving information on HUD. Nevertheless, there is no obvious difference between males and females in terms of specific preferences for the types of displays. The results of this study are expected to provide some inspiration for the design of autonomous driving information on HUD.