See What I See: An Attention-Guiding eHMI Approach for Autonomous Vehicles
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arXiv
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| Autori principali: | , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| _version_ | 1866915811002679296 |
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| author | Li, Jialong Mao, Zhenyu Wang, Zhiyao Lu, Yijun Morita, Shogo Li, Nianyu Tei, Kenji |
| author_facet | Li, Jialong Mao, Zhenyu Wang, Zhiyao Lu, Yijun Morita, Shogo Li, Nianyu Tei, Kenji |
| contents | As autonomous vehicles are gradually being deployed in the real world, external Human-Machine Interfaces (eHMIs) are expected to serve as a critical solution for enhancing vehicle-pedestrian communication. However, existing eHMI designs typically focus solely on the ego vehicle's status, which can inadvertently capture pedestrians' attention or encourage misguided reliance on the AV's signals, leading them to neglect scanning for other surrounding hazards. To address this, we propose the Attention-Guiding eHMI (AGeHMI), a projection-based visualization that employs directional cues and risk-based color coding to actively guide pedestrians' attention toward potential environmental dangers. Evaluation through a virtual reality user study (N = 20) suggests that AGeHMI effectively influences participants' visual attention distribution and significantly reduces potential collision risks with surrounding vehicles, while simultaneously improving subjective confidence and reducing cognitive workload. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_18798 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | See What I See: An Attention-Guiding eHMI Approach for Autonomous Vehicles Li, Jialong Mao, Zhenyu Wang, Zhiyao Lu, Yijun Morita, Shogo Li, Nianyu Tei, Kenji Human-Computer Interaction As autonomous vehicles are gradually being deployed in the real world, external Human-Machine Interfaces (eHMIs) are expected to serve as a critical solution for enhancing vehicle-pedestrian communication. However, existing eHMI designs typically focus solely on the ego vehicle's status, which can inadvertently capture pedestrians' attention or encourage misguided reliance on the AV's signals, leading them to neglect scanning for other surrounding hazards. To address this, we propose the Attention-Guiding eHMI (AGeHMI), a projection-based visualization that employs directional cues and risk-based color coding to actively guide pedestrians' attention toward potential environmental dangers. Evaluation through a virtual reality user study (N = 20) suggests that AGeHMI effectively influences participants' visual attention distribution and significantly reduces potential collision risks with surrounding vehicles, while simultaneously improving subjective confidence and reducing cognitive workload. |
| title | See What I See: An Attention-Guiding eHMI Approach for Autonomous Vehicles |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2602.18798 |