The successful deployment of autonomous vehicles(AVs)relies heavily on their ability to interact safely and effectively with other road users.External human–machine interfaces(eHMIs)have emerged as critical component...The successful deployment of autonomous vehicles(AVs)relies heavily on their ability to interact safely and effectively with other road users.External human–machine interfaces(eHMIs)have emerged as critical components in facilitating these interactions.Rigorous evaluation and testing of eHMIs are essential for realizing their intended safety and communication benefits.This study provides a comprehensive review of current eHMI research,focusing on their impact on road users’behavior and perceptions,as well as the methods used for evaluation.Key behavioral factors—such as eHMI modality,information type,location,vehicle kinematics,traffic environment,and user characteristics—are systematically reviewed and summarized.The influence of eHMIs on user perceptions is also explored through indicators such as perceived safety,comprehensibility,trust,cognitive load,and user experience.Despite notable advancements,several critical research gaps remain underexplored.Most studies focus on one-toone interactions,neglecting the complexities of mixed-traffic environments involving AVs,conventional human-driven vehicles,pedestrians,and cyclists.Current evaluation methods largely rely on virtual reality and Wizard-of-Oz experiments,which may fail to fully capture real-world dynamics.Additionally,subjective questionnaires,which are often used in these studies,do not guarantee high reproducibility of findings.Moreover,insufficient attention has been given to the synchronization of eHMI signals with vehicle kinematics.Furthermore,the absence of standardized evaluation frameworks limits cross-study comparability and the development of universally applicable eHMI solutions.To address these challenges,future research should prioritize the integration of naturalistic traffic scenarios,the adoption of objective and reproducible evaluation methods,the exploration of multimodal eHMI designs,and the development of standardized assessment protocols.These efforts are crucial for improving AV communication with diverse road users and ensuring safety in increasingly complex traffic ecosystems.展开更多
External human–machine interfaces(eHMIs)of highly automated vehicles(HAVs)have been developed to communicate vehicle intentions to pedestrians and other road users.This study conducted an online experiment with 33 pa...External human–machine interfaces(eHMIs)of highly automated vehicles(HAVs)have been developed to communicate vehicle intentions to pedestrians and other road users.This study conducted an online experiment with 33 participants to investigate the impact of a novel HAV’s eHMI of augmented reality(AR)crosswalks on pedestrian crossings using virtual simulation scenario videos and questionnaires.To verify the effectiveness of the AR crosswalk,comparative analyses were performed on eHMIs across 5 modalities:baseline,pedestrian silhouette,virtual eyes,AR headlight,and AR crosswalk,in conjunction with 2 vehicle physical modes(yielding and non-yielding).Metrics used in the study included crossing percentages before the HAV passed,crossing decision time,comprehensibility,and perceived safety.The results indicated that for yielding vehicles,the AR crosswalk resulted in the shortest crossing decision time,the highest comprehensibility,and the highest perceived safety scores.This study provides guidelines for the design of eHMIs and demonstrates that AR eHMIs based on an intelligent vehicle infrastructure cooperative system,can effectively address the one-to-many interaction problem.展开更多
基金supported by the National Natural Science Foundation of China(Nos.52472360,72101128,and 72471070)the China Postdoctoral Science Foundation(No.2023M730560).
文摘The successful deployment of autonomous vehicles(AVs)relies heavily on their ability to interact safely and effectively with other road users.External human–machine interfaces(eHMIs)have emerged as critical components in facilitating these interactions.Rigorous evaluation and testing of eHMIs are essential for realizing their intended safety and communication benefits.This study provides a comprehensive review of current eHMI research,focusing on their impact on road users’behavior and perceptions,as well as the methods used for evaluation.Key behavioral factors—such as eHMI modality,information type,location,vehicle kinematics,traffic environment,and user characteristics—are systematically reviewed and summarized.The influence of eHMIs on user perceptions is also explored through indicators such as perceived safety,comprehensibility,trust,cognitive load,and user experience.Despite notable advancements,several critical research gaps remain underexplored.Most studies focus on one-toone interactions,neglecting the complexities of mixed-traffic environments involving AVs,conventional human-driven vehicles,pedestrians,and cyclists.Current evaluation methods largely rely on virtual reality and Wizard-of-Oz experiments,which may fail to fully capture real-world dynamics.Additionally,subjective questionnaires,which are often used in these studies,do not guarantee high reproducibility of findings.Moreover,insufficient attention has been given to the synchronization of eHMI signals with vehicle kinematics.Furthermore,the absence of standardized evaluation frameworks limits cross-study comparability and the development of universally applicable eHMI solutions.To address these challenges,future research should prioritize the integration of naturalistic traffic scenarios,the adoption of objective and reproducible evaluation methods,the exploration of multimodal eHMI designs,and the development of standardized assessment protocols.These efforts are crucial for improving AV communication with diverse road users and ensuring safety in increasingly complex traffic ecosystems.
基金supported in part by the Natural Science Foundation of China under Grant 52272417in part by the Science and Technology Development Project of Jilin Province under Grant 20230301008ZD.
文摘External human–machine interfaces(eHMIs)of highly automated vehicles(HAVs)have been developed to communicate vehicle intentions to pedestrians and other road users.This study conducted an online experiment with 33 participants to investigate the impact of a novel HAV’s eHMI of augmented reality(AR)crosswalks on pedestrian crossings using virtual simulation scenario videos and questionnaires.To verify the effectiveness of the AR crosswalk,comparative analyses were performed on eHMIs across 5 modalities:baseline,pedestrian silhouette,virtual eyes,AR headlight,and AR crosswalk,in conjunction with 2 vehicle physical modes(yielding and non-yielding).Metrics used in the study included crossing percentages before the HAV passed,crossing decision time,comprehensibility,and perceived safety.The results indicated that for yielding vehicles,the AR crosswalk resulted in the shortest crossing decision time,the highest comprehensibility,and the highest perceived safety scores.This study provides guidelines for the design of eHMIs and demonstrates that AR eHMIs based on an intelligent vehicle infrastructure cooperative system,can effectively address the one-to-many interaction problem.
