Importance of Instruction for Pedestrian-Automated Driving Vehicle Interaction with an External Human Machine Interface: Effects on Pedestrians' Situation Awareness, Trust, Perceived Risks and Decision Making
Compared to a manual driving vehicle (MV), an automated driving vehicle lacks a way to communicate with the pedestrian through the driver when it interacts with the pedestrian because the driver usually does not participate in driving tasks. Thus, an external human machine interface (eHMI) can be viewed as a novel explicit communication method for providing driving intentions of an automated driving vehicle (AV) to pedestrians when they need to negotiate in an interaction, e.g., an encountering scene. However, the eHMI may not guarantee that the pedestrians will fully recognize the intention of the AV. In this paper, we propose that the instruction of the eHMI's rationale can help pedestrians correctly understand the driving intentions and predict the behavior of the AV, and thus their subjective feelings (i.e., dangerous feeling, trust in the AV, and feeling of relief) and decision-making are also improved. The results of an interaction experiment in a road-crossing scene indicate that the participants were more difficult to be aware of the situation when they encountered an AV w/o eHMI compared to when they encountered an MV; further, the participants' subjective feelings and hesitation in decision-making also deteriorated significantly. When the eHMI was used in the AV, the situational awareness, subjective feelings and decision-making of the participants regarding the AV w/ eHMI were improved. After the instruction, it was easier for the participants to understand the driving intention and predict driving behavior of the AV w/ eHMI. Further, the subjective feelings and the hesitation related to decision-making were improved and reached the same standards as that for the MV.
Existing digital technologies in urban settings tend to focus narrowly on concerns around wayfinding, safety, and consumption. In this paper, we examine pedestrian experiences based on the data collected through field observations as well as intensive interviews with nine pedestrians in the Shibuya area of Tokyo, and suggest an alternative approach to blending technologies and urban activities. Our focus is on social and cognitive aspects of pedestrians who get lost and explore a labyrinth of sidewalks. We use the data to discuss the activities that are often ignored or inadequately supported …
Enhancing Safety in Automated Ports: A Virtual Reality Study of Pedestrian-Autonomous Vehicle Interactions under Time Pressure, Visual Constraints, and Varying Vehicle Size
Ningbo University（宁波大学）；Southeast University（东南大学）；University of Macau（澳门大学）；Imperial College London（伦敦帝国学院）；Tongji University（同济大学）
eHMI for All -- Investigating the Effect of External Communication of Automated Vehicles on Pedestrians, Manual Drivers, and Cyclists in Virtual Reality
Institute of Media Informatics, Ulm University（乌尔姆大学媒体信息研究所）；UCL Interaction Centre（伦敦大学学院交互中心）；Eindhoven University of Technology（埃因霍温理工大学）
Occlusion-induced risk and interventions in pedestrian-autonomous truck interactions on multi-lane roads: A virtual reality study
University College London（伦敦大学学院）；Ningbo University（宁波大学）；The University of Hong Kong（香港大学）；Shanghai Maritime University（上海海事大学）；Tongji University（同济大学）
Seoul National University（首尔大学）；Dongguk University（东国大学）
Autonomous vehicles (AVs) can prevent collisions by understanding pedestrian intention. We conducted a virtual reality experiment with 39 participants and measured crossing times (seconds) and head orientation (yaw degrees). We manipulated AV yielding behavior (no-yield, slow-yield, and fast-yield) and the AV size (small, medium, and large). Using machine learning approach, we classified head orientation change of pedestrians by time into 6 clusters of patterns. Results indicate that pedestrian head orientation change was influenced by AV yielding behavior as well as the size of the AV. Partic…
This study aims to understand users' perceptions of using the Dialogflow framework and verify the relationships among service awareness, task-technology fit, output quality, and TAM variables. Generalized Structured Component Analysis was employed to experiment with six hypotheses. Two hundred twenty-seven participants were recruited through the purposive non-random sampling technique. Google Forms was utilized as a medium to develop and distribute survey questionnaires to subjects of interest. The experimental results indicated that perceived ease of use and usefulness had a statistically sig…
Data-driven Causal Discovery for Pedestrians-Autonomous Personal Mobility Vehicle Interactions with eHMIs: From Psychological States to Walking Behaviors
Clemson University（克莱姆森大学）；Stanford University（斯坦福大学）；Arizona State University（亚利桑那州立大学）
There are many unknowns regarding the characteristics and dynamics of human-AI teams, including a lack of understanding of how certain human-human teaming concepts may or may not apply to human-AI teams and how this composition affects team performance. This paper outlines an experimental research study that investigates essential aspects of human-AI teaming such as team performance, team situation awareness, and perceived team cognition in various mixed composition teams (human-only, human-human-AI, human-AI-AI, and AI-only) through a simulated emergency response management scenario. Results …
Michigan State University（密歇根州立大学）；National Technical University of Athens（雅典国家技术大学）；Open University of Cyprus（塞浦路斯开放大学）
The present cross-disciplinary research explores pedestrian-autonomous vehicle interactions in a safe, virtual environment. We first present contemporary tools in the field and then propose the design and development of a new application that facilitates pedestrian point of view research. We conduct a three-step user experience experiment where participants answer questions before and after using the application in various scenarios. Behavioral results in virtuality, especially when there were consequences, tend to simulate real life sufficiently well to make design choices, and we received va…
If They Disagree, Will You Conform? Exploring the Role of Robots' Value Awareness in a Decision-Making Task
CONTACT Unit, Italian Institute of Technology, Genoa, Italy（意大利理工学院CONTACT单元，热那亚，意大利）；IDLab-AIRO, Ghent University – imec, Belgium（根特大学imec IDLab-AIRO，比利时）
Cross-cultural analysis of pedestrian group behaviour influence on crossing decisions in interactions with autonomous vehicles
Universidad de Alcalá（阿尔卡拉大学）；Australian Centre for Field Robotics, University of Sydney（悉尼大学澳大利亚野外机器人中心）；European Commission, Joint Research Centre（欧盟委员会联合研究中心）
Effect of Haptic Feedback on Avoidance Behavior and Visual Exploration in Dynamic VR Pedestrian Environment
Department of Mechano-informatics, The University of Tokyo（机械信息学系，东京大学）；Department of Electrical Engineering, Stanford University（电气工程系，斯坦福大学）；Department of Mechanical Engineering, Stanford University（机械工程系，斯坦福大学）
Misinformation has disruptive effects on our lives. Many researchers have looked into means to identify and combat misinformation in text or data visualization. However, there is still a lack of understanding of how misinformation can be introduced when text and visualization are combined to tell data stories, not to mention how to improve the lay public's awareness of possible misperceptions about facts in narrative visualization. In this paper, we first analyze where misinformation could possibly be injected into the production-consumption process of data stories through a literature survey.…
Swiss Federal School of Technology in Lausanne - EPFL（洛桑联邦理工学院）；University of Tsukuba（筑波大学）；Swiss Federal School of Technology in Zurich - ETH Zurich（苏黎世联邦理工学院）
Autonomous navigation in highly populated areas remains a challenging task for robots because of the difficulty in guaranteeing safe interactions with pedestrians in unstructured situations. In this work, we present a crowd navigation control framework that delivers continuous obstacle avoidance and post-contact control evaluated on an autonomous personal mobility vehicle. We propose evaluation metrics for accounting efficiency, controller response and crowd interactions in natural crowds. We report the results of over 110 trials in different crowd types: sparse, flows, and mixed traffic, with…
Avatar Visual Similarity for Social HCI: Increasing Self-Awareness
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））；LIACS, Universiteit Leiden（莱顿大学LIACS学院）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）；Saarland University（萨尔大学）
Institut de Robòtica i Informàtica Industrial (CSIC-UPC)（工业机器人与信息研究所（西班牙国家研究委员会-加泰罗尼亚理工大学））
In this work we argue that in Human-Robot Collaboration (HRC) tasks, the Perception-Action cycle in HRC tasks can not fully explain the collaborative behaviour of the human and robot and it has to be extended to Perception-Intention-Action cycle, where Intention is a key topic. In some cases, agent Intention can be perceived or inferred by the other agent, but in others, it has to be explicitly informed to the other agent to succeed the goal of the HRC task. The Perception-Intention-Action cycle includes three basic functional procedures: Perception-Intention, Situation Awareness and Action. T…
Passengers of level 3-5 autonomous personal mobility vehicles (APMV) can perform non-driving tasks, such as reading books and smartphones, while driving. It has been pointed out that such activities may increase motion sickness, especially when frequently avoiding pedestrians or obstacles in shared spaces. Many studies have been conducted to build countermeasures, of which various computational motion sickness models have been developed. Among them, models based on subjective vertical conflict (SVC) theory, which describes vertical changes in direction sensed by human sensory organs v.s. those…
