Autonomous vehicles are transforming the transportation industry. In conventional traffic environments, human drivers convey intentions to other pedestrians using gestures and facial expressions. Yet, these traditional interactions, vital for safety, are conspicuously absent in autonomous vehicles, leading to comprehension difficulties and heightened street-crossing risks. While current External Human-Machine Interfaces (eHMIs) aim to mitigate this communication void, they often demand prior familiarization and fall short of intuitiveness, complicating the universal interpretation of a vehicle's intent. To address this, we've developed a novel eHMI for autonomous vehicles, capitalizing on biological motion features. These features, represented by moving dots, capture the movement of key joints in fundamental animal behaviors, such as halting and yielding. Drawing from leopards' skeletal and motion patterns, our bio-motion eHMI integrates animal communication metaphors, like 'please let me pass' and 'I will yield,' to enhance clarity in vehicle-pedestrian interactions. We investigate whether integrating these animal-inspired biological motion patterns into autonomous vehicles can bolster pedestrian comprehension of vehicle intent and movement, ultimately fostering safer street-crossing behaviors. 32 Chinese participants engaged in the experiment online, observing video clips that demonstrated vehicular movements via our eHMI. Subsequently, they answered multiple-choice questions assessing their understanding of the vehicle's movement and intent. The results show that the Bio-Motion eHMI significantly outperforms both Text eHMI and Non-display in interpreting vehicle movement. Moreover, both Bio-Motion eHMI and Text eHMI excel over Non-display in discerning vehicle intent. Impressively, the bio-motion eHMI not only stands out in accuracy concerning vehicle intent and movement but also garners superior subjective preferences compared to other interfaces. In conclusion, our biologically-inspired motion-centric eHMI presents a natural conduit for vehicle-to-pedestrian communication, ensuring swift and precise comprehension of vehicle intentions. This pioneering approach has the potential to revolutionize external vehicle interfaces, marking a new chapter in inclusive design within the autonomous vehicle realm.