{"ID":2823996,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.24166","arxiv_id":"2512.24166","title":"External Human-Machine Interface based on Intent Recognition: Framework Design and Experimental Validation","abstract":"Increasing autonomous vehicles (AVs) in transportation systems makes effective interactions between AVs and pedestrians indispensable. External human--machine interface (eHMI), which employs visual or auditory cues to explicitly convey vehicle behaviors can compensate for the loss of human-like interactions and enhance AV--pedestrian cooperation. To facilitate faster intent convergence between pedestrian and AVs, this study incorporates an adaptive interaction mechanism into eHMI based on pedestrian intent recognition, namely IR-eHMI. IR-eHMI dynamically detects and infers the behavioral intentions of both pedestrians and AVs through identifying their cooperation states. The proposed interaction framework is implemented and evaluated on a virtual reality (VR) experimental platform to demonstrate its effectiveness through statistical analysis. Experimental results show that IR-eHMI significantly improves crossing efficiency, reduces gaze distraction while maintaining interaction safety compared to traditional fixed-distance eHMI. This adaptive and explicit interaction mode introduces an innovative procedural paradigm for AV--pedestrian cooperation.","short_abstract":"Increasing autonomous vehicles (AVs) in transportation systems makes effective interactions between AVs and pedestrians indispensable. External human--machine interface (eHMI), which employs visual or auditory cues to explicitly convey vehicle behaviors can compensate for the loss of human-like interactions and enhance...","url_abs":"https://arxiv.org/abs/2512.24166","url_pdf":"https://arxiv.org/pdf/2512.24166v1","authors":"[\"Boya Sun\",\"Haotian Shi\",\"Ying Ni\",\"Shaocheng Jia\",\"Haoyang Liang\"]","published":"2025-12-30T11:52:07Z","proceeding":"cs.HC","tasks":"[\"cs.HC\"]","methods":"[]","has_code":false}