Evaluating Automotive Augmented Reality Head-up Display Effects on Driver Performance and Distraction

Faria, Nayara de Oliveira

doi:10.1109/vrw50115.2020.00128

Cited by 7 publications

(4 citation statements)

References 8 publications

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“…Driver distraction is not a new safety problem, and these glances are much longer than what is considered dangerous in manual driving (e.g., over 1.7 to 2.0 seconds; [14]). Although there is no safe glance threshold for automated or vehicles with AR cueing [15], this result still suggests that partial automation has the potential to contribute to driver underload associated with low levels of engagement with the driving environment. As driving systems become more automated, driving demands are reduced, which on the one hand can be helpful in complex and challenging driving situations.…”

Section: Discussionmentioning

confidence: 91%

The Effect of Augmented Reality Cues on Glance Behavior and Driver-Initiated Takeover on SAE Level 2 Automated-Driving

Faria

Merenda²,

Greatbatch

et al. 2021

Proceedings of the Human Factors and Ergonomics Society Annual

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In the present paper, we present a user study with an advanced-driver assistance system (ADAS) using augmented reality (AR) cues to highlight pedestrians and vehicles when approaching intersections of varying complexity. Our major goal is to understand the relationship between the presence and absence of AR, driver-initiated takeover rates and glance behavior when using a SAE Level 2 autonomous vehicle. Therefore, a user-study with eight participants on a medium-fidelity driving simulator was carried out. Overall, we found that AR cues can provide promising means to increase the system transparency, drivers’ situation awareness and trust in the system. Yet, we suggest that the dynamic glance allocation of attention during partially automated vehicles is still challenging for researchers as we still have much to understand and explore when AR cues become a distractor instead of an attention guider.

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Section: Discussionmentioning

confidence: 91%

The Effect of Augmented Reality Cues on Glance Behavior and Driver-Initiated Takeover on SAE Level 2 Automated-Driving

Faria

Merenda²,

Greatbatch

et al. 2021

Proceedings of the Human Factors and Ergonomics Society Annual

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“…This approach lacks thorough consideration, affecting the authenticity and effectiveness of evaluation results. Given the visual confusion resulting from suboptimal HUD interface layouts 21 and the increased cognitive load due to unreasonable spatial allocation, a comprehensive evaluation of interface layouts is necessary, considering both subjective and objective perspectives.…”

Section: Related Workmentioning

confidence: 99%

Cognitive evaluation of HUD interface layout for intelligent automotive based on Bayesian BWM and Gray-TOPSIS

Zhang,

Wang,

et al. 2024

Advances in Mechanical Engineering

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To reduce drivers’ cognitive load during the driving process, The present study concentrates on the cognitive evaluation and analysis of the Head-Up Display (HUD) interface layout, aiming to enhance human cognitive efficiency. Initially, a combination of eye-tracking technology and cognitive load theory is used to investigate users’ attention allocation and changes in eye movement indicators, followed by the conversion of these indicators. A comprehensive HUD interface layout evaluation system is established, considering structural layout esthetics, task efficiency, and cognitive load. To achieve this, an intelligent cognitive evaluation method for the automotive HUD interface layout is proposed, based on the Bayesian BWM and Gray-TOPSIS. Bayesian BWM is employed to determine the weights of evaluation indicators, followed by Gray-TOPSIS to assess and rank the layout candidate solutions. Experimental results indicate that in the optimal layout design, users exhibit fewer eye movements, shorter gaze durations, esthetically pleasing interface structures, and lower cognitive loads. Furthermore, comparative experiments validate the effectiveness and stability of the Bayesian BWM and Gray-TOPSIS methods. These findings offer guidance and reference for further optimizing the layout of intelligent automotive HUD interfaces.

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“…Driving is a multi-sensory task, including visual, auditory, and tactile sensations. According to past studies, many accidents occur due to distraction or fatigue [1], although the use of head-up displays has been shown to distract drivers [2]- [4], it can reduce the number of times looking down at conventional dashboards and reduce gaze movement [5], [6]. Therefore, the development of driving aids has become an important issue in order to reduce the driver's burden.…”

Section: Introductionmentioning

confidence: 99%

26‐3: Assessing Vehicle Driver Performance in Dual‐Depth Head‐Up Displays

Chou,

Chuang,

Hsu

et al. 2023

Symp Digest of Tech Papers

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In recent years, the display information of automotive head‐up displays has gradually developed from single depth to multiple depths. To reduce the driver's workload and reduce the number of eye adjustments, the researchers used the human eye's visual perception to realize the image information that blends with the real world. The results of the study showed that the dual‐depth head‐up display was effective in reducing driver workload.

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Evaluating Automotive Augmented Reality Head-up Display Effects on Driver Performance and Distraction

Cited by 7 publications

References 8 publications

The Effect of Augmented Reality Cues on Glance Behavior and Driver-Initiated Takeover on SAE Level 2 Automated-Driving

The Effect of Augmented Reality Cues on Glance Behavior and Driver-Initiated Takeover on SAE Level 2 Automated-Driving

Cognitive evaluation of HUD interface layout for intelligent automotive based on Bayesian BWM and Gray-TOPSIS

26‐3: Assessing Vehicle Driver Performance in Dual‐Depth Head‐Up Displays

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