Vulnerable road users and the coming wave of automated vehicles: Expert perspectives

Automated driving research over the past decades has mostly focused on highway environments. Recent technological developments have drawn researchers and manufacturers to look ahead at introducing automated driving in cities. The current position paper examines this challenge from the viewpoint of scientific experts. Sixteen Human Factors researchers were interviewed about their personal perspectives on automated vehicles (AVs) and the interaction with VRUs in the future urban environment. Aspects such as smart infrastructure, external human-machine interfaces (eHMIs), and the potential of augmented reality (AR) were addressed during the interviews. The interviews showed that the researchers believed that fully autonomous vehicles will not be introduced in the coming decades and that intermediate levels of automation, specific AV services, or shared control will be used instead. The researchers foresaw a large role of smart infrastructure and expressed a need for AV-VRU segregation, but were concerned about corresponding costs and maintenance requirements. The majority indicated that eHMIs will enhance future AV-VRU interaction, but they noted that implicit communication will remain dominant and advised against text-based and instructive eHMIs. AR was commended for its potential in assisting VRUs, but given the technological challenges, its use, for the time being, was believed to be limited to scientific experiments. The present expert perspectives may be instrumental to various stakeholders and researchers concerned with the relationship between VRUs and AVs in future urban traffic.

[1]  John D. Lee,et al.  Augmented Reality Cues and Elderly Driver Hazard Perception , 2013, Hum. Factors.

[2]  Ronald Azuma,et al.  A Survey of Augmented Reality , 1997, Presence: Teleoperators & Virtual Environments.

[3]  D. W. F. van Krevelen,et al.  A Survey of Augmented Reality Technologies, Applications and Limitations , 2010, Int. J. Virtual Real..

[4]  Riender Happee,et al.  Passenger opinions of the perceived safety and interaction with automated shuttles: A test ride study with ‘hidden’ safety steward , 2020, Transportation Research Part A: Policy and Practice.

[5]  Julia Fink,et al.  Anthropomorphism and Human Likeness in the Design of Robots and Human-Robot Interaction , 2012, ICSR.

[6]  Klaus Bengler,et al.  Why Do I Have to Drive Now? Post Hoc Explanations of Takeover Requests , 2018, Hum. Factors.

[7]  Joost C. F. de Winter,et al.  Survey on eHMI concepts: The effect of text, color, and perspective , 2019, Transportation Research Part F: Traffic Psychology and Behaviour.

[8]  Avinoam Borowsky,et al.  Deep Reinforcement Learning for Human-Like Driving Policies in Collision Avoidance Tasks of Self-Driving Cars , 2020, ArXiv.

[9]  Hermann Winner,et al.  Three Decades of Driver Assistance Systems: Review and Future Perspectives , 2014, IEEE Intelligent Transportation Systems Magazine.

[10]  Catherine M. Burns,et al.  Autonomous Driving in the Real World: Experiences with Tesla Autopilot and Summon , 2016, AutomotiveUI.

[11]  Lesley Strawderman,et al.  Efficacy of virtual reality in pedestrian safety research. , 2017, Applied ergonomics.

[12]  W. Haddon,et al.  On the escape of tigers: an ecologic note. , 1970, American journal of public health and the nation's health.

[13]  Joost C. F. de Winter,et al.  Adaptive automation: automatically (dis)engaging automation during visually distracted driving , 2018, PeerJ Comput. Sci..

[14]  Klaus Bengler,et al.  Virtually the same? Analysing pedestrian behaviour by means of virtual reality , 2020 .

[15]  John Dixon,et al.  The Design of Future Things , 2010 .

[16]  Neville A. Stanton,et al.  Take-Over Time in Highly Automated Vehicles , 2018, Driver Reactions to Automated Vehicles.

[17]  Alexandra Neukum,et al.  Driver compliance to take-over requests with different auditory outputs in conditional automation. , 2017, Accident; analysis and prevention.

[18]  Julio A. Sanguesa,et al.  Advances in smart roads for future smart cities , 2020, Proceedings of the Royal Society A.

[19]  Johan F. Hoorn,et al.  Distributed cognition , 2005, Cognition, Technology & Work.

[20]  Takeo Igarashi,et al.  Eyes on a Car: an Interface Design for Communication between an Autonomous Car and a Pedestrian , 2017, AutomotiveUI.

[21]  Oliver M. J. Carsten,et al.  How can humans understand their automated cars? HMI principles, problems and solutions , 2019, Cognition, Technology & Work.

[22]  Stephen M. Casner,et al.  The challenges of partially automated driving , 2016, Commun. ACM.

[23]  Alois Ferscha,et al.  Augmented reality navigation systems , 2006, Universal Access in the Information Society.

[24]  Jochen Seitz,et al.  Vehicle-to-Pedestrian Communication for Vulnerable Road Users: Survey, Design Considerations, and Challenges , 2019, Sensors.

[25]  Yuxi Li,et al.  Deep Reinforcement Learning , 2018, Reinforcement Learning for Cyber-Physical Systems.

[26]  Alex Fridman,et al.  To Walk or Not to Walk: Crowdsourced Assessment of External Vehicle-to-Pedestrian Displays , 2017, ArXiv.

[27]  Malcolm Skinner,et al.  Dictionary of Geography , 1999 .

[28]  Natasha Merat,et al.  Designing the interaction of automated vehicles with other traffic participants: design considerations based on human needs and expectations , 2018, Cognition, Technology & Work.

[29]  Kathrin Zeeb,et al.  What determines the take-over time? An integrated model approach of driver take-over after automated driving. , 2015, Accident; analysis and prevention.

[30]  Neville A. Stanton,et al.  Turing in the driver's seat: Can people distinguish between automated and manually driven vehicles? , 2020, Human Factors and Ergonomics in Manufacturing & Service Industries.

[31]  Wilbert Tabone The Effectiveness of an Augmented Reality Guiding System in an Art Museum , 2020 .

[32]  Hans-Peter Schöner,et al.  The Behavioral Validity of Dual-Task Driving Performance in Fixed and Moving Base Driving Simulators , 2016 .

[33]  Natasha Merat,et al.  Testing external HMI designs for automated vehicles – An overview on user study results from the EU project interact , 2019 .

[34]  Lin Wang,et al.  Ready to bully automated vehicles on public roads? , 2020, Accident; analysis and prevention.

[35]  Susan Mayhew,et al.  A Dictionary of Geography , 1997 .

[36]  P A Hancock,et al.  The Effects of Virtual Reality, Augmented Reality, and Mixed Reality as Training Enhancement Methods: A Meta-Analysis , 2020, Hum. Factors.

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

[38]  Matthias Beggiato,et al.  An experimental study to investigate design and assessment criteria: What is important for communication between pedestrians and automated vehicles? , 2019, Applied ergonomics.

[39]  Shancang Li,et al.  5G Internet of Things: A survey , 2018, J. Ind. Inf. Integr..

[40]  Jeffrey G. Andrews,et al.  What Will 5G Be? , 2014, IEEE Journal on Selected Areas in Communications.

[41]  Neville A. Stanton,et al.  Distributed Cognition on the road: Using EAST to explore future road transportation systems. , 2018, Applied ergonomics.

[42]  Y. B. Eisma,et al.  External Human-Machine Interfaces: The Effect of Display Location on Crossing Intentions and Eye Movements , 2019, Inf..

[43]  Malte Risto,et al.  The social behavior of autonomous vehicles , 2016, UbiComp Adjunct.

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

[45]  Thomas B. Sheridan,et al.  A critique of the SAE conditional driving automation definition, and analyses of options for improvement , 2018, Cognition, Technology & Work.

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

[47]  Yuzhong Shen,et al.  AR-PED: A framework of augmented reality enabled pedestrian-in-the-loop simulation , 2019, Simul. Model. Pract. Theory.

[48]  Joshua E. Domeyer,et al.  Vehicle Automation–Other Road User Communication and Coordination: Theory and Mechanisms , 2020, IEEE Access.

[49]  Melissa Cefkin,et al.  Multi-methods Research to Examine External HMI for Highly Automated Vehicles , 2019, HCI.

[50]  Yao-Ting Sung,et al.  Development and behavioral pattern analysis of a mobile guide system with augmented reality for painting appreciation instruction in an art museum , 2014, Comput. Educ..

[51]  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.

[52]  Paul C. Schutte,et al.  The H-Metaphor as a Guideline for Vehicle Automation and Interaction , 2005 .

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

[54]  Araz Taeihagh,et al.  Governing autonomous vehicles: emerging responses for safety, liability, privacy, cybersecurity, and industry risks , 2018, Transport Reviews.

[55]  Natasha Merat,et al.  Defining interactions: a conceptual framework for understanding interactive behaviour in human and automated road traffic , 2020 .

[56]  Joan Severson,et al.  Validation of virtual reality as a tool to understand and prevent child pedestrian injury. , 2008, Accident; analysis and prevention.

[57]  Neville A. Stanton,et al.  Sub-systems on the road to vehicle automation: Hands and feet free but not 'mind' free driving , 2014 .

[58]  Muztaba Fuad,et al.  Comparison of Child and Adult Pedestrian Perspectives of External Features on Autonomous Vehicles Using Virtual Reality Experiment , 2019, AHFE.

[59]  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.

[60]  Don Norman,et al.  The challenges of automation in the automobile , 2019, Ergonomics.

[61]  Marjan Hagenzieker,et al.  Automated bus systems in Europe: A systematic review of passenger experience and road user interaction , 2020 .

[62]  J. Gibson The Ecological Approach to Visual Perception , 1979 .

[63]  Brian R. Duffy,et al.  Anthropomorphism and the social robot , 2003, Robotics Auton. Syst..

[64]  Wendy Ju,et al.  Emergency, Automation Off: Unstructured Transition Timing for Distracted Drivers of Automated Vehicles , 2015, 2015 IEEE 18th International Conference on Intelligent Transportation Systems.

[65]  R. Ross,et al.  Position Statement Identification and Management of Cardiometabolic Risk in Canada: A Position Paper by the Cardiometabolic Risk Working Group (Executive Summary) , 2011 .

[66]  Ji Hyun Yang,et al.  Takeover Requests in Simulated Partially Autonomous Vehicles Considering Human Factors , 2017, IEEE Transactions on Human-Machine Systems.

[67]  R. Happee,et al.  A human factors perspective on automated driving , 2017 .

[68]  Lorenz Prasch,et al.  Analyzing Pedestrian Behavior in Augmented Reality — Proof of Concept , 2020, 2020 IEEE Conference on Virtual Reality and 3D User Interfaces (VR).

[69]  Sally A. Applin,et al.  Extending Driver-Vehicle Interface Research Into the Mobile Device Commons : Transitioning to (nondriving) passengers and their vehicles. , 2015, IEEE Consumer Electronics Magazine.

[70]  David Sirkin,et al.  The Case for Implicit External Human-Machine Interfaces for Autonomous Vehicles , 2019, AutomotiveUI.

[71]  Klaus Bengler,et al.  Comparison of Methods to Evaluate the Influence of an Automated Vehicle's Driving Behavior on Pedestrians: Wizard of Oz, Virtual Reality, and Video , 2020, Inf..

[72]  Donald A. Norman,et al.  Turn Signals Are The Facial Expressions Of Automobiles , 1992 .

[73]  P. Hancock Driving Into the Future , 2020, Frontiers in Psychology.

[74]  Keiichi Uchimura,et al.  Driver Inattention Monitoring System for Intelligent Vehicles: A Review , 2009, IEEE Transactions on Intelligent Transportation Systems.

[75]  P. Delrio,et al.  Achieving high quality standards in laparoscopic colon resection for cancer: A Delphi consensus-based position paper. , 2018, European journal of surgical oncology : the journal of the European Society of Surgical Oncology and the British Association of Surgical Oncology.

[76]  Thomas B. Sheridan,et al.  Human and Computer Control of Undersea Teleoperators , 1978 .

[77]  Florian Alt,et al.  A Design Space for External Displays on Cars , 2017, AutomotiveUI.

[78]  Klaus Bengler,et al.  Pedestrian simulators for traffic research: State of the art and future of a motion lab , 2018 .

[79]  Victoria A Banks,et al.  Is partially automated driving a bad idea? Observations from an on-road study. , 2018, Applied ergonomics.

[80]  M. Hannan,et al.  2019 EACTS Expert Consensus on long-term mechanical circulatory support , 2019, European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery.

[81]  Federico Manuri,et al.  A Survey on Applications of Augmented Reality , 2016 .

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

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

[84]  Jonas Bärgman,et al.  Holistic assessment of driver assistance systems: how can systems be assessed with respect to how they impact glance behaviour and collision avoidance? , 2020 .

[85]  Fabian Kröger,et al.  Automated Driving in Its Social, Historical and Cultural Contexts , 2016 .

[86]  Stewart A. Birrell,et al.  Driving Style: How Should an Automated Vehicle Behave? , 2019, Inf..

[87]  Albrecht Schmidt,et al.  Implicit human computer interaction through context , 2000, Personal Technologies.

[88]  Philipp Wintersberger,et al.  Taming the eHMI jungle: A classification taxonomy to guide, compare, and assess the design principles of automated vehicles' external human-machine interfaces , 2020, Transportation Research Interdisciplinary Perspectives.

[89]  Håkan Alm,et al.  External Human–Machine Interfaces for Autonomous Vehicle-to-Pedestrian Communication: A Review of Empirical Work , 2019, Front. Psychol..

[90]  Neville A. Stanton,et al.  Rolling Out the Red (and Green) Carpet: Supporting Driver Decision Making in Automation-to-Manual Transitions , 2019, IEEE Transactions on Human-Machine Systems.

[91]  Mark Mulder,et al.  Haptic shared control: smoothly shifting control authority? , 2012, Cognition, Technology & Work.

[92]  Mark S. Young,et al.  Drive-by-wire: The case of driver workload and reclaiming control with adaptive cruise control , 1997 .

[93]  Claudia,et al.  Vehicle Movement and its Potential as Implicit Communication Signal for Pedestrians and Automated Vehicles , 2022 .

[94]  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.