Stereoscopic 3D dashboards

Weidner, Florian; Broll, Wolfgang · 2020 · Crossref

DOI: 10.1007/s00779-020-01438-8

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Summary

This study investigates the efficacy of stereoscopic 3D (S3D) dashboards for presenting smart take-over requests (TORs) in semi-autonomous driving. The research addresses the critical challenge of restoring driver situation awareness (SA) when humans must resume control from an automated vehicle, particularly when engaged in non-driving-related tasks. The authors aim to determine if visual TORs that provide spatial information about the traffic environment, displayed in S3D, improve take-over performance and safety compared to traditional 2D or simple warning icons, without increasing mental workload. The researchers conducted a driving simulator study using a 4 × 2 between-within design with 52 participants. Participants performed an n-back task to simulate being "out of the loop" while the vehicle operated in conditional automation mode. The study compared four TOR conditions: a baseline simple icon, and three "smart" TORs displaying relative vehicle positions and environmental data. These smart TORs were presented in either perspective 3D (P3D/2D) or stereoscopic 3D (S3D) at three dashboard locations: the instrument cluster (TOR-IC), the driver’s focus of attention (TOR-FoA), and a large center stack area (TOR-Large). Mental workload was assessed using ocular measures, specifically pupil diameter and blink behavior, while take-over performance was evaluated based on driving metrics and gaze behavior. The results indicate that smart visual TORs significantly improved take-over performance compared to the baseline, as participants effectively processed the visual information to execute safer maneuvers. S3D warnings generally outperformed P3D counterparts. Specifically, warnings presented at the driver’s focus of attention and at the instrument cluster yielded the best performance outcomes. Crucially, the addition of complex visual information did not increase mental workload, as evidenced by stable ocular measures. The study also identified three distinct gaze patterns associated with processing these warnings, highlighting how drivers interact with in-vehicle visual cues during critical transitions. The findings suggest that S3D dashboards are a valid and effective option for supporting take-overs in automated vehicles. By providing spatial context without overloading cognitive resources, smart S3D TORs can enhance safety and situation assessment. The study concludes that well-designed visual warnings, particularly those leveraging depth cues and strategic placement, can mitigate the risks associated with delayed situation awareness during control transfers. This work contributes to the development of automotive user interfaces by demonstrating that advanced visualization techniques can support driver performance without detrimental side effects on workload or secondary task engagement.

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Topics

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• visual
• hud ar windshield
• temporal
• visual occlusion
• attention allocation
• in vehicle displays

Information type

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• Empirical Findings: behavioral performance data
• Methodological Resource: measurement protocol, tool software