IEEE IV 2022 Workshop on

Are You Happy with AV ?

User Experience (UX) in AV-Human Interaction

June 5th, 2022 – Aachen, Germany

Room F203, ika, RWTH Aachen University (Full day session)



The prospect of wide-scale development of automated vehicles (AVs) is due to advances in, e.g., robotics, computational power, communications, sensor technologies, and the recognition performance by using artificial intelligence technology. AV brings novel interactions to human users as drivers, passengers, and other human traffic partners undoubtedly. Human-machine interface (HMI) and recommendations of AV kinematics behavior have been widely studied to build appropriate interaction and communication between AV and human users to calibrate trust and improve actual and perceived safety, efficiency, comfort, prosociality, etc. User experience (UX) correlates trust and has mutual influence with multiple factors, including motion sickness, comfort, and other subjective feelings. However, it is still unclear how to achieve a better-integrated user experience in AV-human interactions. How UX and other human factors influence each other is widely unknown. This workshop aims to bring together multidisciplinary researchers from academia and industry to discuss the impact factors and potential approaches to improve user experience inside and outside automated vehicles. It is an open platform that includes interdisciplinary researchers from engineering, cognitive psychology, computer science, informatics, sociology, and design to integrate the ideas and obtain inspirations.


  • What factors from cognition perspective influence ‘user experience’ in AV-human interaction?

  • What is needed to create a positive user experience of automated driving?

  • What is the relationship between UX and other human factors?

  • How is mutual influence among comfort, trust, actual & perceived safety, workload, situation awareness, enjoyment, efficiency, satisfaction and prosociality?

  • Human-AV interaction theories, concepts, and models

  • UX design and evaluations

  • Evaluation metrics

  • Human factors

  • Cognitive psychology

  • User experience and usability

  • Human-machine interface for AV

  • Kinematics of AV

  • User comfort

  • Trust in AV

Session 1. Motion Sickness in AV Comfort

Modeling Motion Sickness toward Comfortable Automated Vehicles

-Motion Sickness to Motion Happiness-

Prof. Dr. Takahiro Wada

He is a professor at Nara Institute of Science and Technology (NAIST) in Japan. He focuses on the fundamental research on the intellectualization of mechanical systems, understanding human skillful motion and intelligent/natural behaviours, and human-machine systems / human-robot collaboration. He is interested in building methodologies for designing comfortable and useful robots and human-machine systems.


Automated driving systems release human drivers from driving tasks and allow them to enjoy their subtasks. Besides, there is concern that these changes will lead to an increase in motion sickness, and the related research studies have drawn much attention. Recall that the purpose of automated driving is to reduce the driving workload and improve productivity during transportation. An increase in motion sickness in automated driving is clearly contrary to this. Quantification of motion sickness is important to improve vehicle motion in automated driving. In this talk, I will introduce research studies on computational modeling of motion sickness as a technique for quantifying it and its application to automatic driving.


Asst. Prof. Dr. Hailong LIU

He is an assistant professor at Graduate School of Science and Technology, NAIST, Japan. He received his Ph.D. degrees in Engineering from Ritsumeikan University, Japan in 2018. His research interests include machine learning and deep learning to analyze driving behavior, and over-trust, eHMI, motion sickness in human-autonomous vehicle interactions.


Passengers (drivers) of level 3-5 autonomous personal mobility vehicles (APMV) and cars 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. Many studies have been conducted to build countermeasures, of which various computational motion sickness models have been developed. Many of these are based on subjective vertical conflict (SVC) theory, which describes vertical changes in direction sensed by human sensory organs vs. those expected by the central nervous system. Such models are expected to be applied to autonomous driving scenarios. However, no current computational model can integrate visual vertical information with vestibular sensations. We proposed a 6 DoF SVC-VV model which add a visually perceived vertical block into a conventional six-degrees-of-freedom SVC model to predict VV directions from image data simulating the visual input of a human. Hence, a simple image-based VV estimation method is proposed. As the validation of the proposed model, this paper focuses on describing the fact that the motion sickness increases as a passenger reads a book while using an AMPV, assuming that visual vertical (VV) plays an important role. In the static experiment, it is demonstrated that the estimated VV by the proposed method accurately described the gravitational acceleration direction with a low mean absolute deviation. In addition, the results of the driving experiment using an APMV demonstrated that the proposed 6 DoF SVC-VV model could describe that the increased motion sickness experienced when the VV and gravitational acceleration directions were different.


Revisiting the design of automated vehicles for enhancing motion comfort

Dr. Georgios Papaioannou

He is a Postdoctoral Researcher at KTH Royal Institute of Technology in Sweden within the ECO2 Vehicle Design Center. He studied Mechanical Engineering at National Technical University of Athens (NTUA), from where he also received his PhD in 2019. His PhD was awarded the second prize of the Gkiourounlian Scholarship by NTUA praising its potential industrial impact and contribution. He is an Associate Editor at SAE International Journal of Connected and Automated Vehicles and SAE International Journal of Passenger Vehicle Systems. His current research interests include motion comfort in automated and human driven vehicles, human perception, vehicle dynamics, chassis and seat design, optimization, control and motion planning.


Automated vehicles (AVs) are considered as one of the major technological developments within the automotive industry, able to influence future mobility and improve life quality. At the same time, there are still important challenges that need to be overcome before AVs be part of our daily life. The ability to engage in non-driving activities during the ride is considered by consumers one of the key reasons for adoption of AVs, while motion comfort is among the most important factors for affecting public trust. Hence, a refocus on motion comfort is crucial. Regarding its assessment and modelling, there are no models incorporating posture maintenance factors, and a first attempt is briefly described in this talk. Regarding the countermeasures to enhance motion comfort, the loss of controllability is the paradigm shift from the role of humans as drivers, to the role of passengers in automated vehicles. Due to this, the users might perceive the AVs driving style more aggressive. Hence, the need to consider requirements related to motion comfort in the motion-planning control modules has emerged. At the same time, the velocity decrease as a countermeasure for motion sickness incidence could cause dissatisfaction to the occupants as the journey time will increase. Therefore, other solutions should be considered. AVs are expected to have complete interior redesign for people to engage in non-driving activities while being driven. The additional space will allow the inclusion of larger seats which could employ suspension systems that can help us further enhance motion comfort.


Session 2. Communication between AV and Road Users

Are you yielding for me or not? The role of kinematics information and eHMI

Dr. Yee Mun Lee

She is currently a senior research fellow at the Institute for Transport Studies, University of Leeds. She obtained her BSc (Hons) in Psychology and her PhD degree in driving cognition from The University of Nottingham Malaysia in 2012 and 2016 respectively. Her current research interests include investigating the interaction between automated vehicles and other road users, by using various methods, especially virtual reality experimental designs. Yee Mun was the leader of the 'Methodologies, Evaluation and Impact Assessment' Work Package of the EU-funded project, interACT. She was also involved in another EU-funded project, L3Pilot, where she investigated the Users' Evaluation and Experience of a Level 3 system. She is now a Co-lead of the User Sub-project of another EU-funded project, Hi-Drive. Finally, Yee Mun is one of the SHAPE-IT project supervisors, where she continues her research on Human interaction with AVs in Urban Scenarios, and also actively involved in the International Organisation for Standardisation (ISO).


In the future, Automated Vehicles (AVs) will need to interact with other road users, such as cyclists, pedestrians, and other vehicles. To enhance safety, improve traffic flow, and increase user acceptance and trust in AVs; pedestrians and other road users need to understand the AVs' intentions, communication, and behaviour. Kinematics cues was found to be the most important and used information when pedestrians make a crossing decision. However, perceiving kinematics cues can sometimes be challenging. This presentation will look into how implicit cues (i.e., vehicle movement) and eHMIs affect pedestrians' crossing behaviour and subjective evaluations; specifically looking at scenarios in which an approaching vehicle decelerates but does not always intend to yield. How would pedestrians react to these situations? Can they accurately judge the intention of the approaching decelerating vehicles? How do eHMIs affect pedestrians' crossing decisions?

YM IEEE IV presentation 2022.pdf

Designing Automated Vehicles as the Prosocial Actors of Future Traffic

Dr. Shadan Sadeghian

She is a Postdoc researcher in the department of “Ubiquitous Design / Experience and Interaction” led by Prof. Dr Marc Hassenzahl at the University of Siegen, Germany. She studied computer science at the University of Bonn and RWTH Aachen, and pursued her PhD in Human-Computer Interaction at the OFFIS Institute for Information Technology at the University of Oldenburg. She worked as a PhD scholar in the Max Planck Institute in Tübingen and later as a Postdoc researcher at Fraunhofer institute FKIE.


Road traffic is a social situation where many participants are expected to interact with caution and mutual respect. Consequently, communication plays an important role. Typically, the communication between pedestrians and drivers is nonverbal and consists of a combination of gestures, eye contact, and body movement. With increasing levels of vehicle automation, and decreasing human control and engagement in the driving task itself, this communication will inevitably change. The question is how “mindful” and “prosocial” automated vehicles (AV) behave and communicate in traffic. In the last years, several researchers studied the design of communication between AVs and other road users. However, it remains unclear, how this impacts the perceptions of the quality of communication and impressions of mindfulness and prosociality. While the decisions related to the design of technology for automated vehicles are mostly technical, they have social consequences.


Heavy, bold and kind: Interactions between heavy automated vehicles and other road users

Dr. Azra Habibovic

She is currently Technology Leader for Human Factors at Scania CV where she is responsible for defining and driving forward Scania’s roadmap for research in this area. Prior to joining Scania, she was with RISE Research Institutes of Sweden as senior researcher and project leader, and with the research center SAFER as research area director for road user behavior. She holds a PhD in Vehicle Safety Systems (2012) and a MSc in Electrical and Electronics Engineering (2006), both from Chalmers University of Technology, Sweden. Her research focuses on improving traffic safety and user experience by means of automation and connectivity. A special interest is design and evaluation of interactions in and around automated vehicles, including interactions with vulnerable road users.


A great majority of the existing research on interactions between automated vehicles and other road users addresses interactions between automated passenger cars and pedestrians. However, it is largely unknown whether insights from these studies are transferable to heavy automated vehicles or other traffic contexts. At the same time, development of heavy automated vehicles is being intensified due to business opportunities that such vehicles are likely to unlock. In this presentation, I’ll discuss potential challenges when it comes to interactions between heavy automated vehicles and other road users. I’ll also present some preliminary results from our recent studies where we explored the effect of additional external communication signals on such interactions, both in urban and highway contexts. The studies have been conducted within the research project External interaction principles for creating trust in heavy automated vehicles (co-financed by Strategic vehicle research and innovation program FFI).

2022-06-05 IV WS_Scania_Azra Habibovic.pdf

Session 3. Trust and Acceptance in Human-AV Interactions

Need-based User Experience Research for Automated Driving

Dr. Philipp Wintersberger

He is a researcher at TU Wien (Vienna University of Technology). He obtained his doctorate in Engineering Science from Johannes Kepler University Linz, specializing in Human-Machine Cooperation. His publications, which focus on trust in automation, attentive user interfaces, transparency of driving algorithms, as well as UX and acceptance of automated vehicles, have received several awards in the past years. He has co-organized multiple workshops at well-known HCI conferences, served as Technical Program Chair for AutomotiveUI’21, and is a member of the AutomotiveUI steering committee.


User acceptance and experience of technology are firmly based on satisfying basic psychological needs. A need-based development approach in the design of automated vehicles can reveal emerging problems and pain points and allow the development of control interfaces beyond what the SAE levels of automation typically offer. In this presentation, I will show the results of different user studies that utilized this method in different levels of automation, ranging from emerging trust issues at SAE level 2, user experience issues in driver take-over at SAE level 3, and the benefits of shared control at SAE level 5.


Building Trust in Automation considering Driver Uncertainty and System Uncertainty

Dr. Fei Yan

She is currently a postdoctoral researcher at the Department of Human Factors, Institute of Psychology and Education, Ulm University. She received her PhD degree in Human Factors focusing on driver uncertainty during decision-making from Ulm University, Germany. Her main research interests include modeling driver uncertainty, trust in automation, adaptive driver assistance in automated driving, cross-cultural studies of driving behavior.


Inspired by the emancipation theory of trust in social psychology, it is proposed to build trust with the consideration of driver uncertainty and system uncertainty, which is demonstrated by empirical studies. For instance, driver's trust in automation can be built by developing a lane change assistance system that can adapt to driver uncertainty during decision-making in lane change maneuvers. Furthermore, it is also shown that spatially visualizing system's uncertainty can influence driver's decisions for actions, which implies the change of trust in automation behind it.


Ambient Light Displays for Automated Driving

Dr. Andreas Löcken

Dr. Andreas Löcken is a postdoctoral researcher in the HCI group at the Technische Hochschule Ingolstadt (THI) and the CARISSMA Institute of Automated Driving (C-IAD). Before Ingolstadt, he studied and worked at the University of Oldenburg and the OFFIS Institute for Information Technology in Oldenburg, Germany. He received his doctoral degree for his dissertation on ambient light displays in vehicles.

His current research focuses on human-computer interfaces, especially the interaction between humans and automated vehicles, including the perspective of passengers and vulnerable road users such as pedestrians.


As automation increases, drivers are increasingly engaged in tasks other than driving. However, depending on the level of information, the passenger may still need to monitor the vehicle's decisions or be prepared to take control. Ambient displays can help here, conveying information without distracting drivers from their primary task. Even in fully automated driving, ambient displays can provide feedback on the vehicle's state to increase confidence in the vehicle or serve as a modality for novel applications with new goals, like increasing comfort or decreasing motion sickness.

Focusing on ambient light displays, I will introduce ambient displays and discuss their design possibilities in cars. In addition, I will give examples from previous research and point out future directions for ambient displays in the era of automated driving.



Hailong Liu

Nara Institute of Science and Technology (NAIST),
  • Machine Learning
  • Human-Machine Interaction

Hao Cheng

Leibniz University Hannover,


  • Machine Learning
  • Road User Behavior Prediction

Ruolin Gao

Eindhoven University of Technology,

the Netherlands

  • Human Factors
  • User Experience
  • Automotive UI

Yang Li

Karlsruhe Institute of Technology (KIT),


  • Automotive UI
  • User Experience