Programs

Openning (5min)

Invited presentation 1 (15min+5min)

Enhancing Road Safety in Automated Driving: 

Exploring the Human Factor, Vulnerable Road Users, and Mixed Traffic Scenarios 

Abstract:

As the field of automated driving continues to evolve, it becomes increasingly evident that understanding the role of human behavior in automated driving systems is crucial. This presentation explores the dynamics of mixed traffic scenarios, where automated vehicles interact with human-operated vehicles, navigating complex urban environments and varied traffic conditions. By conducting a thorough examination of the challenges posed by vulnerable road users, including pedestrians and cyclists, this talk aims to offer an understanding of the challenges and opportunities in enhancing road safety considering automated driving technologies.

Invited presentation 2 (15min+5min)

Research on Human-Machine Collaborative Interaction Design for Intelligent Driving

Abstract:

The design of intelligent cockpits aims to provide users with a comfortable and pleasant driving experience. To achieve this, it is necessary to adhere to the principles of ergonomics, which involve considering both the physical characteristics of the human body and researching human perception and psychological traits, such as the behaviors, emotions, and personal choices of drivers and passengers. Human-machine interaction systems serve as bridges between humans and intelligent cockpits, enabling precise bidirectional communication of intentions and information between users and the system, thereby facilitating the completion of various tasks during dynamic driving processes.

The presentation will start from these requirements and share insights from laboratory research experience on intelligent driving human-machine cooperative interaction design. We will discuss research perspectives, system models, and information architecture design for the cognitive interface of intelligent cockpits. Within the realm of cognitive interface interaction design, two major themes of intelligent cockpit human-machine cooperative interaction will be explored: 1) Attention Space and Virtual Mixed Information, and 2) Cognitive Interaction Models of Virtual Humans in the Intelligent Cockpit. Real-life cases and experiments conducted in the laboratory will be used to illustrate each aspect in detail.

Contributed Paper presentation (15min+5min)

An eHMI Presenting Request-To-Intervene Status of Level 3 Automated Vehicles to Surrounding Vehicles

Masaki Kuge 1, Hailong Liu 1, Toshihiro Hiraoka 2, Takahiro Wada 1

(1. Nara Institute of Science and Technology, Japan. 2. Japan Automobile Research Institute,Japan)  

Abstract:

This study takes a fresh perspective by focusing on the drivers of surrounding cars near to level 3 automated vehicles (AVs). We advocates for level 3 AVs using an external human-machine interface (eHMI) to provide high-risk warning information to drivers of surrounding cars during AVs issuing a request-to-intervene (RtI). Through a driving simulator-based subjects experiments, we have established that the proposed eHMI can assist the surrounding MV's driver in better comprehending the AV's driving intentions and predicting its driving behavior. This leads to increased the surrounding MV's driver confidence in handling potential risks from the AV during the take-over period. While we did not observe a significant impact of the proposed eHMI on the driving behavior of the MV drivers, i.e. participants, they reported a greater willingness to have AVs equipped with the proposed eHMI drive around them in their daily life.

Workshop paper 1 (15min+5min)

Towards Human-Like Autonomous Vehicles:

 A Qualitative Evaluation and Design Perspective

Jemin Woo, Changsun Ahn

(Pusan National University, Republic of Korea)

Abstract:

This study proposes a method for qualitatively evaluating and designing human-like driver models for autonomous vehicles. While most existing research on human-likeness has been focused on quantitative evaluation, it is crucial to consider qualitative measures to accurately capture human perception. To this end, we administered surveys to participants to discern whether the driver was human or autonomous. The survey employed direct experiential evaluation by participants. The findings of this research can significantly contribute to the development of naturalistic and human-like driver models for autonomous vehicles, enabling them to safely and efficiently coexist with human drivers in diverse driving scenarios through the interaction between human and autonomous vehicles.

Invited presentation 3 (15min+5min)

Motion Comfort in Vehicles based on Computational Understanding of Human Motion Perception and Sickness 

Abstract:

In our research project, "Challenges for Human-Machine Matching through Computational Modeling of Human Motion Perception and Motion Control", we are conducting studies on human-machine matching from the perspectives of motion perception and sickness models. This presentation focuses on introducing the computational models of motion perception and motion sickness as the basis of the projects and their applications, highlighting their use for vehicle passengers. These include presenting models of motion sickness and demonstrating their capabilities in predicting human motion perception and eye movements. The potential application of the models for various techniques to reduce motion sickness will be introduced, including detection methods and generating motion signals for sickness reduction.

Invited presentation 4 (15min+5min)

The use of Generative AI and Large Language models for UX Design in Automotive Applications – An introduction & tutorial 

Abstract:

The field of User Experience (UX) development in Automotive is rapidly evolving. The recent rise of Generative AI and Large Language Models (LLMs) has opened new capabilities in UX design and development. This keynote explores the multifaceted applications of these technologies offering practical insights and tutorial-like examples for effectively harnessing Generative AI power. The talk will include innovative applications such as generating personas for in-cabin user interaction, creating visual assets to streamline design experimentation, and supporting the development, and testing of UI code. Each of these areas will be illustrated with concrete examples, demonstrating the transformative potential of Generative AI in speeding the ability of researchers and developers to craft more intuitive and user-centered automotive experiences. Furthermore, the keynote will address the challenges and opportunities presented by more advanced Generative AI applications, such as finetuning, Retrieval-Augmented Generation (RAG), model ensembles, and handling multimodality. Through this exploration, we aim to illuminate the path toward more sophisticated use of Generative AI in automotive UX design, paving the way for future advancements and a deeper understanding of these groundbreaking technologies.

Workshop paper 2 (15min+5min)

Driving the Future: Addressing Generational Trust and Ownership Barriers in the Adoption of Connected and Autonomous Vehicles

Clare Mutzenich, Fergus McVey, Ceire Martin, Claire Harding

 (7th Sense Research, United Kingdom)

Abstract:

The advent of Connected and Autonomous Vehicles (CAVs) promises a transformative shift in transport, ushering in an era of shared mobility and interconnectedness. However, recent events highlight the challenges facing the widespread acceptance of CAVs and Mobility as a Service (MaaS). Drawing on insights from a survey of over 3,000 transport users in the UK, our study reveals two significant hurdles impeding the transition to shared autonomy. Firstly, user adoption presents a formidable challenge, slowing the pace of relinquishing control to driverless mobility, due to low trust and acceptance across generational groups. Secondly, the enduring preference for private vehicle ownership acts as a barrier to embracing shared mobility solutions. To address these challenges, we introduce the SASS Model, which categorises individuals into four distinct groups based on their emotional and rational inclinations towards CAVs and MaaS: Sceptics, Alarmists, Swing Voters, and Supporters. Each group necessitates a tailored approach to effectively communicate the benefits of CAVs. Strategies include addressing the concerns of Alarmists, providing reassurance to Sceptics, educating Swing Voters about the advantages of an automated future, and leveraging the backing of Supporters. Ultimately, the momentum of the automated (r)evolution will be driven by rational Sceptics and Swing Voters, highlighting the importance of targeted messaging and positioning by the automotive industry and transport providers to secure widespread adoption. 

Workshop paper 3 (15min+5min)

A Meaningful Human Control Perspective on User Perception of Partially Automated Driving Systems: 

A Case Study of Tesla Users

Lucas Elbert Suryana, Sina Nordhoff, Simeon Craig Calvert, Arkady Zgonnikov, Bart van Arem

(Delft University of Technology, The Netherlands)  

Abstract:

The use of partially automated driving systems raises concerns about potential responsibility issues, posing risk to the system safety, acceptance, and adoption of these technologies. The concept of meaningful human control has emerged in response to the responsibility gap problem, requiring the fulfillment of two conditions, tracking and tracing. While this concept has provided important philosophical and design insights on automated driving systems, there is currently little knowledge on how meaningful human control relates to subjective experiences of actual users of these systems. To address this gap, our study aimed to investigate the alignment between the degree of meaningful human control and drivers’ perceptions of safety and trust in a real-world partially automated driving system. We utilized previously collected data from interviews with Tesla ``Full Self-Driving'' (FSD) Beta users, investigating the alignment between the user perception and how well the system was tracking the users' reasons. We found that tracking of users' reasons for driving tasks (such as safe maneuvers) correlated with perceived safety and trust, albeit with notable exceptions. Surprisingly, failure to track lane changing and braking reasons was not necessarily associated with negative perceptions of safety. However, the failure of the system to track expected maneuvers in dangerous situations always resulted in low trust and perceived lack of safety. Overall, our analyses highlight alignment points but also possible discrepancies between perceived safety and trust on the one hand, and meaningful human control on the other hand. Our results can help the developers of automated driving technology to design systems under meaningful human control and are perceived as safe and trustworthy.