APA
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Chang, X., Chen, Z., Dong, X., Cai, Y., Yan, T., Cai, H., … Gong, J. (2024). "{It} {Must} {Be} {Gesturing} {Towards} {Me}": {Gesture}-{Based} {Interaction} between {Autonomous} {Vehicles} and {Pedestrians}. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (pp. 1–25). New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3613904.3642029
Chicago/Turabian
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Chang, Xiang, Zihe Chen, Xiaoyan Dong, Yuxin Cai, Tingmin Yan, Haolin Cai, Zherui Zhou, Guyue Zhou, and Jiangtao Gong. “&Quot;{It} {Must} {Be} {Gesturing} {Towards} {Me}&Quot;: {Gesture}-{Based} {Interaction} between {Autonomous} {Vehicles} and {Pedestrians}.” In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems, 1–25. CHI '24. New York, NY, USA: Association for Computing Machinery, 2024.
MLA
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Chang, Xiang, et al. “&Quot;{It} {Must} {Be} {Gesturing} {Towards} {Me}&Quot;: {Gesture}-{Based} {Interaction} between {Autonomous} {Vehicles} and {Pedestrians}.” Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems, Association for Computing Machinery, 2024, pp. 1–25, doi:10.1145/3613904.3642029.
BibTeX Click to copy
@inproceedings{chang2024a,
title = {"{It} {Must} {Be} {Gesturing} {Towards} {Me}": {Gesture}-{Based} {Interaction} between {Autonomous} {Vehicles} and {Pedestrians}},
year = {2024},
month = may,
address = {New York, NY, USA},
pages = {1--25},
publisher = {Association for Computing Machinery},
series = {CHI '24},
doi = {10.1145/3613904.3642029},
author = {Chang, Xiang and Chen, Zihe and Dong, Xiaoyan and Cai, Yuxin and Yan, Tingmin and Cai, Haolin and Zhou, Zherui and Zhou, Guyue and Gong, Jiangtao},
booktitle = {Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems},
month_numeric = {5}
}
Interacting with pedestrians understandably and efficiently is one of the toughest challenges faced by autonomous vehicles (AVs) due to the limitations of current algorithms and external human-machine interfaces (eHMIs). In this paper, we design eHMIs based on gestures inspired by the most popular method of interaction between pedestrians and human drivers. Eight common gestures were selected to convey AVs’ yielding or non-yielding intentions at uncontrolled crosswalks from previous literature. Through a VR experiment (N1 = 31) and a following online survey (N2 = 394), we discovered significant differences in the usability of gesture-based eHMIs compared to current eHMIs. Good gesture-based eHMIs increase the efficiency of pedestrian-AV interaction while ensuring safety. Poor gestures, however, cause misinterpretation. The underlying reasons were explored: ambiguity regarding the recipient of the signal and whether the gestures are precise, polite, and familiar to pedestrians. Based on this empirical evidence, we discuss potential opportunities and provide valuable insights into developing comprehensible gesture-based eHMIs in the future to support better interaction between AVs and other road users.