Calibrating Pedestrians' Trust in Automated Vehicles: Does an Intent Display in an External HMI Support Trust Calibration and Safe Crossing Behavior?

Policymakers recommend that automated vehicles (AVs) display their automated driving status using an external human-machine interface (eHMI). However, previous studies suggest that a status eHMI is associated with overtrust, which might be overcome by an additional yielding intent message. We conducted a video-based laboratory study (N = 67) to investigate pedestrians’ trust and crossing behavior in repeated encounters with AVs. In a 2x2 between-subjects design, we investigated (1) the occurrence of a malfunction (AV failing to yield) and (2) system transparency (status eHMI vs. status+intent eHMI). Results show that during initial encounters, trust gradually increases and crossing onset time decreases. After a malfunction, trust declines but recovers quickly. In the status eHMI group, trust was reduced more, and participants showed 7.3 times higher odds of colliding with the AV as compared to the status+intent group. We conclude that a status eHMI can cause pedestrians to overtrust AVs and advocate additional intent messages.

[1]  Klaus Bengler,et al.  From HMI to HMIs: Towards an HMI Framework for Automated Driving , 2020, Inf..

[2]  Bastian Pfleging,et al.  Color and Animation Preferences for a Light Band eHMI in Interactions Between Automated Vehicles and Pedestrians , 2020, CHI.

[3]  Sowmya Somanath,et al.  Communicating Awareness and Intent in Autonomous Vehicle-Pedestrian Interaction , 2018, CHI.

[4]  P. Mayring Qualitative content analysis: theoretical foundation, basic procedures and software solution , 2014 .

[5]  Lu Wang,et al.  Trust and Reliance on an Automated Combat Identification System , 2009, Hum. Factors.

[6]  J. Kraus,et al.  What’s Driving Me? Exploration and Validation of a Hierarchical Personality Model for Trust in Automated Driving , 2020, Hum. Factors.

[7]  David P. Biros,et al.  The Influence of Task Load and Automation Trust on Deception Detection , 2004 .

[8]  Fabrice Vienne,et al.  Trust and the use of adaptive cruise control: a study of a cut-in situation , 2006, Cognition, Technology & Work.

[9]  Johannes Kraus,et al.  Psychological processes in the formation and calibration of trust in automation , 2020 .

[10]  A. Field Discovering statistics using IBM SPSS statistics, 5th edition , 2017 .

[11]  Mica R. Endsley,et al.  Toward a Theory of Situation Awareness in Dynamic Systems , 1995, Hum. Factors.

[12]  Josef F. Krems,et al.  Driving and Situation Awareness: A Cognitive Model of Memory-Update Processes , 2009, HCI.

[13]  Andreas Butz,et al.  Supporting Trust in Autonomous Driving , 2017 .

[14]  Sarah Schmidt,et al.  Pedestrians at the kerb – Recognising the action intentions of humans , 2009 .

[15]  Tim Schmitz,et al.  Contrast Analysis Focused Comparisons In The Analysis Of Variance , 2016 .

[16]  Daniel W. Carruth,et al.  Investigating pedestrian suggestions for external features on fully autonomous vehicles: A virtual reality experiment , 2018, Transportation Research Part F: Traffic Psychology and Behaviour.

[17]  N. Moray,et al.  Trust in automation. Part II. Experimental studies of trust and human intervention in a process control simulation. , 1996, Ergonomics.

[18]  Jacques M. B. Terken,et al.  Gaze Patterns in Pedestrian Interaction with Vehicles: Towards Effective Design of External Human-Machine Interfaces for Automated Vehicles , 2019, AutomotiveUI.

[19]  Yee Mun Lee,et al.  External Human–Machine Interfaces Can Be Misleading: An Examination of Trust Development and Misuse in a CAVE-Based Pedestrian Simulation Environment , 2020, Hum. Factors.

[20]  M. S. Patel,et al.  An introduction to meta-analysis. , 1989, Health Policy.

[21]  Rikard Fredriksson,et al.  Will There Be New Communication Needs When Introducing Automated Vehicles to the Urban Context , 2017 .

[22]  Martin Baumann,et al.  A Longitudinal Video Study on Communicating Status and Intent for Self-Driving Vehicle – Pedestrian Interaction , 2020, CHI.

[23]  Berry Eggen,et al.  The Impact of Vehicle Appearance and Vehicle Behavior on Pedestrian Interaction with Autonomous Vehicles , 2017, AutomotiveUI.

[24]  Jacob Cohen,et al.  A power primer. , 1992, Psychological bulletin.

[25]  Pavlo Bazilinskyy,et al.  External Human-Machine Interfaces: Which of 729 Colors Is Best for Signaling ‘Please (Do not) Cross’? , 2020, 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[26]  Andreas Butz,et al.  Overtrust in External Cues of Automated Vehicles: An Experimental Investigation , 2019, AutomotiveUI.

[27]  Irene Gohl,et al.  Analyzing driver-pedestrian interaction at crosswalks: A contribution to autonomous driving in urban environments , 2016, 2016 IEEE Intelligent Vehicles Symposium (IV).

[28]  Raymond H. Cuijpers,et al.  The Effects of Explicit Intention Communication, Conspicuous Sensors, and Pedestrian Attitude in Interactions with Automated Vehicles , 2020, CHI.

[29]  Eva-Maria Messner,et al.  Scared to Trust? – Predicting Trust in Highly Automated Driving by Depressiveness, Negative Self-Evaluations and State Anxiety , 2020, Frontiers in Psychology.

[30]  Gloria Pöhler,et al.  Itemanalyse und Faktorstruktur eines Fragebogens zur Messung von Vertrauen im Umgang mit automatischen Systemen , 2016 .

[31]  Azra Habibovic,et al.  SAV2P: Exploring the Impact of an Interface for Shared Automated Vehicles on Pedestrians' Experience , 2017, AutomotiveUI.

[32]  J. Krems,et al.  The evolution of mental model, trust and acceptance of adaptive cruise control in relation to initial information , 2013 .

[33]  Rikard Fredriksson,et al.  Communicating Intent of Automated Vehicles to Pedestrians , 2018, Front. Psychol..

[34]  Riender Happee,et al.  External Human-Machine Interfaces on Automated Vehicles: Effects on Pedestrian Crossing Decisions , 2019, Hum. Factors.

[35]  Yuan Liu,et al.  External Interface-based Autonomous Vehicle-to-Pedestrian Communication in Urban Traffic: Communication Needs and Design Considerations , 2020, Int. J. Hum. Comput. Interact..

[36]  Masooda N. Bashir,et al.  Trust in Automation: Integrating Empirical Evidence on Factors That Influence Trust , 2015, Hum. Factors.

[37]  Alex Fridman,et al.  Eye Contact Between Pedestrians and Drivers , 2019, Proceedings of the 10th International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design: driving assessment 2019.

[38]  Martin Baumann,et al.  Two Routes to Trust Calibration: Effects of Reliability and Brand Information on Trust in Automation , 2019, Int. J. Mob. Hum. Comput. Interact..

[39]  Francesco Biondi,et al.  Human–Vehicle Cooperation in Automated Driving: A Multidisciplinary Review and Appraisal , 2019, Int. J. Hum. Comput. Interact..

[40]  Stefanie M. Faas,et al.  Yielding Light Signal Evaluation for Self-driving Vehicle and Pedestrian Interaction , 2019, IHSED.

[41]  Mark R. Lehto,et al.  Foundations for an Empirically Determined Scale of Trust in Automated Systems , 2000 .

[42]  Peter A. M. Ruijten,et al.  Enhancing Trust in Autonomous Vehicles through Intelligent User Interfaces That Mimic Human Behavior , 2018, Multimodal Technol. Interact..

[43]  Stewart A. Birrell,et al.  Calibrating trust through knowledge: Introducing the concept of informed safety for automation in vehicles , 2018, Transportation Research Part C: Emerging Technologies.

[44]  N Moray,et al.  Trust, control strategies and allocation of function in human-machine systems. , 1992, Ergonomics.

[45]  Andrii Matviienko,et al.  Communicating the intention of an automated vehicle to pedestrians: The contributions of eHMI and vehicle behavior , 2020, it Inf. Technol..

[46]  Josef F. Krems,et al.  A Comprehension Based Cognitive Model of Situation Awareness , 2009, HCI.

[47]  Martin Baumann,et al.  External HMI for self-driving vehicles: Which information shall be displayed? , 2020 .

[48]  Arne Jönsson,et al.  Wizard of Oz studies: why and how , 1993, IUI '93.

[49]  Ralf Risser,et al.  Pedestrian-driver communication and decision strategies at marked crossings. , 2017, Accident; analysis and prevention.

[50]  Michael Sivak,et al.  Road safety with self-driving vehicles: general limitations and road sharing with conventional vehicles , 2015 .

[51]  Moritz Körber,et al.  Introduction matters: Manipulating trust in automation and reliance in automated driving. , 2018, Applied ergonomics.

[52]  Andreas Riener,et al.  Strategies for Negotiation between Autonomous Vehicles and Pedestrians , 2015, MuC Workshopband.

[53]  Zhixia Li,et al.  Exploring the mechanism of crashes with automated vehicles using statistical modeling approaches , 2019, PloS one.

[54]  Martin Baumann,et al.  Light-Based External Human Machine Interface: Color Evaluation for Self-Driving Vehicle and Pedestrian Interaction , 2019, Proceedings of the Human Factors and Ergonomics Society Annual Meeting.

[55]  Bonnie M. Muir,et al.  Trust Between Humans and Machines, and the Design of Decision Aids , 1987, Int. J. Man Mach. Stud..

[56]  Jacques M. B. Terken,et al.  Pedestrian Interaction with Vehicles: Roles of Explicit and Implicit Communication , 2017, AutomotiveUI.

[57]  Regina A. Pomranky,et al.  The role of trust in automation reliance , 2003, Int. J. Hum. Comput. Stud..

[58]  D. Cicchetti Guidelines, Criteria, and Rules of Thumb for Evaluating Normed and Standardized Assessment Instruments in Psychology. , 1994 .

[59]  Darren George,et al.  SPSS for Windows Step by Step: A Simple Guide and Reference , 1998 .

[60]  Adam Millard-Ball,et al.  Pedestrians, Autonomous Vehicles, and Cities , 2016 .

[61]  John D. Lee,et al.  Trust in Automation: Designing for Appropriate Reliance , 2004, Hum. Factors.

[62]  Jonas Andersson,et al.  External Vehicle Interfaces for Communication with Other Road Users? , 2018, Lecture Notes in Mobility.

[63]  Moritz Körber,et al.  Theoretical Considerations and Development of a Questionnaire to Measure Trust in Automation , 2018, Advances in Intelligent Systems and Computing.

[64]  Mark A. Daly Task Load and Automation Use in an Uncertain Environment , 2012 .

[65]  Bonnie M. Muir,et al.  Trust in automation. I: Theoretical issues in the study of trust and human intervention in automated systems , 1994 .

[66]  Natasha Merat,et al.  What externally presented information do VRUs require when interacting with fully Automated Road Transport Systems in shared space? , 2018, Accident; analysis and prevention.

[67]  Wendy Ju,et al.  Ghost driver: A field study investigating the interaction between pedestrians and driverless vehicles , 2016, 2016 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN).

[68]  Johannes Kraus,et al.  The More You Know: Trust Dynamics and Calibration in Highly Automated Driving and the Effects of Take-Overs, System Malfunction, and System Transparency , 2019, Hum. Factors.

[69]  Klaus Bengler,et al.  Trust in Automation – Before and After the Experience of Take-over Scenarios in a Highly Automated Vehicle☆ , 2015 .

[70]  Daniel J. Fagnant,et al.  Preparing a Nation for Autonomous Vehicles: Opportunities, Barriers and Policy Recommendations , 2015 .

[71]  Raja Parasuraman,et al.  Humans and Automation: Use, Misuse, Disuse, Abuse , 1997, Hum. Factors.

[72]  Yung-Ching Liu,et al.  Risk Analysis of Pedestrians’ Road-Crossing Decisions: Effects of Age, Time Gap, Time of Day, and Vehicle Speed , 2014 .

[73]  Klaus Bengler,et al.  Taxonomy of Traffic Situations for the Interaction between Automated Vehicles and Human Road Users , 2017 .

[74]  Nicolas Guéguen,et al.  A pedestrian’s stare and drivers’ stopping behavior: A field experiment at the pedestrian crossing , 2015 .