How Do We Study Pedestrian Interaction with Automated Vehicles? Preliminary Findings from the European interACT Project

This paper provides an overview of a set of behavioural studies, conducted as part of the European project interACT, to understand road user behaviour in current urban settings. The paper reports on a number of methodologies used to understand how humans currently interact in urban traffic, in order to establish what information would be useful for the design of future AVs, when interacting with other road users, especially pedestrians. In addition to summarising the results from a number of observation studies, we report on preliminary results from Virtual Reality studies, investigating if, in the absence of a human vehicle controller, externally presented interfaces can be used for communication between AVs and pedestrians. Finally, an overview of the mathematical and computational modelling techniques used to understand how AV and pedestrian behaviour can be both cooperative, and effective is provided. The hope is that future AVs can be designed with an understanding of how humans cooperate and communicate in mixed traffic, promoting good traffic flow, user acceptance and user trust.

[1]  Scott D. Brown,et al.  Diffusion Decision Model: Current Issues and History , 2016, Trends in Cognitive Sciences.

[2]  Lenja Sorokin,et al.  A Change of Perspective: Designing the Automated Vehicle as a New Social Actor in a Public Space , 2019, CHI Extended Abstracts.

[3]  Bernhard Friedrich,et al.  A multi-layer social force approach to model interactions in shared spaces using collision prediction , 2017 .

[4]  Natasha Merat,et al.  Investigating Pedestrians' Crossing Behaviour During Car Deceleration Using Wireless Head Mounted Display: An Application Towards the Evaluation of eHMI of Automated Vehicles , 2019, Proceedings of the 10th International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design: driving assessment 2019.

[5]  Washington Y. Ochieng,et al.  Modelling shared space users via rule-based social force model , 2015 .

[6]  Richard P. Heitz,et al.  Neurally constrained modeling of perceptual decision making. , 2010, Psychological review.

[7]  Natasha Merat,et al.  Filtration analysis of pedestrian-vehicle interactions for autonomous vehicle control , 2018 .

[8]  Ding Zhao,et al.  Evaluation of automated vehicles encountering pedestrians at unsignalized crossings , 2017, 2017 IEEE Intelligent Vehicles Symposium (IV).

[9]  Natasha Merat,et al.  Predicting pedestrian road-crossing assertiveness for autonomous vehicle control , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[10]  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.

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

[12]  Monika Sester,et al.  Mixed Traffic Trajectory Prediction Using LSTM-Based Models in Shared Space , 2018, AGILE Conf..

[13]  Natasha Merat,et al.  When Should the Chicken Cross the Road? - Game Theory for Autonomous Vehicle - Human Interactions , 2018, VEHITS.

[14]  Natasha Merat,et al.  Models of Human Decision-Making as Tools for Estimating and Optimizing Impacts of Vehicle Automation , 2018, Transportation Research Record: Journal of the Transportation Research Board.

[15]  Josep Perarnau,et al.  The need to combine different traffic modelling levels for effectively tackling large-scale projects adding a hybrid meso/micro approach , 2011 .

[16]  Michael J. Flannagan,et al.  Behavioral Adaptation to Advanced Driver Assistance Systems: A Literature Review , 2016 .

[17]  Natasha Merat,et al.  Sustained sensorimotor control as intermittent decisions about prediction errors: computational framework and application to ground vehicle steering , 2017, Biological Cybernetics.

[18]  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.

[19]  Jonas Andersson,et al.  Evaluating interactions with non-existing automated vehicles: three Wizard of Oz approaches , 2016, 2016 IEEE Intelligent Vehicles Symposium (IV).

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

[21]  D. Helbing Traffic and related self-driven many-particle systems , 2000, cond-mat/0012229.

[22]  Malte Risto,et al.  Human-Vehicle Interfaces: The Power of Vehicle Movement Gestures in Human Road User Coordination , 2017 .

[23]  André Dietrich,et al.  Observing Traffic -- Utilizing a Ground Based LiDAR and Observation Protocols at a T-Junction in Germany , 2018 .

[24]  Michael P. Clamann,et al.  Evaluation of Vehicle-to-Pedestrian Communication Displays for Autonomous Vehicles , 2017 .

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

[26]  Natasha Merat,et al.  Empirical game theory of pedestrian interaction for autonomous vehicles , 2018 .

[27]  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.

[28]  W. Horrey,et al.  The effect of conformity tendency on pedestrians' road-crossing intentions in China: an application of the theory of planned behavior. , 2009, Accident; analysis and prevention.