External Human-Machine Interface based on Intent Recognition: Framework Design and Experimental Validation

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.