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

Zhou, Chengmin; Luo, Yuxuan; Kaner, Jake · 2025 · OpenAlex-citations

DOI: 10.1371/journal.pone.0329760

archive: archived pipeline: cataloged verified

Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)

Summary

This study investigates how different Head-up Display (HUD) types affect drivers’ Situation Awareness (SA) and take-over performance in semi-autonomous vehicles, specifically under varying driving scenario complexities. As Level 2+ autonomous driving systems become prevalent, maintaining driver SA during control handovers is critical for safety. The research addresses two primary questions: which HUD display type best enhances SA and take-over ability, and how the effectiveness of these displays changes with scenario complexity. The study focuses on three display types prevalent in the Chinese automotive market: Pseudo-3D (P3D) on Widescreen HUDs, Surround Recognition (SR) on Widescreen HUDs, and Augmented Reality (AR) on AR-HUDs. The researchers conducted a simulation experiment using a Latin square design, integrating eye-tracking technology with the Situation Awareness Global Assessment Technique (SAGAT). Scenario complexity was objectively determined using the Entropy Weight Method (EWM) based on expert evaluations of road, traffic, and environmental factors, categorizing scenarios into low, medium, and high complexity. The study involved young drivers, with sample size determined via GPower analysis. The experimental design controlled for autonomous driving functions (AEB, ACC, LKA) to isolate the impact of display types. Results indicate that AR displays generally yielded better performance for young drivers, particularly in high-complexity scenarios. Specifically, fixation duration was significantly shorter with AR displays compared to P3D displays in high-complexity situations (P = 0.012), suggesting more efficient information processing. However, the advantage of AR diminished as scenario complexity decreased. Conversely, SR displays negatively impacted performance; they were associated with significantly higher fixation counts than P3D and AR displays (P < 0.001), indicating reduced SA, and longer take-over reaction times compared to AR displays (P = 0.09), particularly in medium-complexity scenarios. Gender analysis revealed that male participants paid more attention to HUD information, though no significant gender differences were found in display type preferences. The findings suggest that AR-HUDs are the most effective for maintaining driver SA and ensuring efficient take-overs in complex driving environments, while SR displays may hinder performance by overloading visual attention. The study concludes that display effectiveness is context-dependent, with AR advantages most pronounced in high-complexity scenarios. These results provide empirical evidence for optimizing Human-Machine Interface designs in semi-autonomous vehicles, recommending AR technology for critical safety information presentation to support driver situational awareness and reaction efficiency.

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.

Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• hud ar windshield
• situational awareness
• in vehicle displays
• automation

Information type

What kind of knowledge this paper contributes, grouped by family — independent of topic (what it is about) and method (how it was studied).

• Empirical Findings: behavioral performance data
• Methodological Resource: measurement protocol
• Theoretical Contribution: conceptual framework