Effects of a color gradient and emoji in AR-HUD warning interfaces in autonomous vehicles on takeover performance and driver emotions

Abstract

Objective

This study examined the effects of color gradients and emojis in an augmented reality-head-up display (AR-HUD) warning interface on driver emotions and takeover performance.

Methods

A total of 48 participants were grouped into four different warning interfaces for a simulated self-driving takeover experiment. Two-way analysis of variance and the Kruskal–Wallis test was used to analyze takeover time, mood, task load, and system availability.

Results

Takeover efficiency and task load did not significantly differ among the interfaces, but the interfaces with a color gradient and emoji positively affected drivers’ emotions. Emojis also positively affected emotional valence, and the color gradient had a high emotional arousal effect. Both the color gradient and the emoji interfaces had an inhibitory effect on negative emotions. The emoji interface was easier to learn, reducing driver learning costs.

Conclusions

These findings offer valuable insights for designing safer and more user-friendly AR-HUD interfaces for self-driving cars.

Keywords:

• Autonomous vehicles
• AR-HUD
• takeover performance
• emotions
• color gradient
• emoji

Acknowledgments

We thank LetPub (www.letpub.com) for its linguistic assistance during the preparation of this manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The datasets generated and/or analyzed during the current study are available from the corresponding author upon reasonable request.

Additional information

Funding

This work was supported by the National Key R&D Program of China (grant number: 2022YFB4301202-14; 2022YFB4301201-20; 2022YFB4301203-16); the National Natural Science Foundation of China (grant number 52175253); the Project of Sichuan Natural Science Foundation (Youth Science Foundation) (grant number 22NSFSC0865); the Project of Sichuan Provincial Key Laboratory of Digital Media Art, the, Sichuan Conservatory of Music (grant number 22DMAKL02); the Degree and Postgraduate Education and Teaching Reform Project of Southwest Jiaotong University (grant number YJG5-2022-Y038); the Project of Design-AI Lab of the China Academy of Art (grant number CAADAI2022B002); Southwest Jiaotong University New Interdisciplinary Cultivation Fund Program (grant number YG2022007); and the Project of the Systems Science and Enterprise Development Research Center (grant number Xq23B08).