While the Society of Automotive Engineers (SAE) International’s classification system (J3016) has provided a framework for categorising sustained driving automation systems, concerns have arisen about its clarity and ability to incorporate emerging technologies . Therefore, this study explores how various stakeholders, including end users, vehicle manufacturers, and policymakers, use the driving automation taxonomy. The results show that driving automation taxonomy is communicated through media, incorporated into vehicle purchasing decisions for users, and utilised for external and internal communication by vehicle manufacturers and policymakers. The discussion highlights that utilising specialised terminology in automation enhances communication efficiency. However, there is also a discrepancy between the SAE J3016, which is today’s prevalent taxonomy, and their audience in terms of both (1) clarity provided by the taxonomy vs. understanding of the stakeholders and (2) topics addressed by the taxonomy vs. needs of the stakeholders. The study also highlights that, while SAE J3016 is being criticised, proposing a clearly better taxonomy is far from straightforward. However, we underscore the importance of revising and updating the current taxonomy to align with stakeholder needs and technological advancements. By enhancing the clarity and relevance of the driving automation taxonomy, stakeholders can make more informed decisions, fostering innovation and improving communication across the industry.

As Advanced Driver Assistance Systems (ADAS) become integrated into vehicles, driver education is important to support the safe and effective use of these technologies. However, structured ADAS educational programs for drivers have not been extensively studied. Moreover, the perspective of driving instructors, key stakeholders in the training process, has been overlooked. To address this gap, this study explores the perspectives of professional driving instructors who have delivered structured ADAS driver training at driving academies across four European countries. Through semi-structured interviews with fourteen instructors, this study examines the impact of the training, training design, implementation challenges, demographic considerations, and institutional roles. Instructors reported that ADAS driver training enhances driver confidence and promotes the appropriate use of the system, particularly by reducing overreliance on automation. They also emphasised the importance of a phased training model, combining theoretical instruction, controlled on-track practice, and on-road driving. In addition, Instructors highlighted the need for tailored approaches for older drivers and for introducing ADAS training after novice drivers have acquired basic driving skills. This study suggests the need for standardised ADAS training and cross-sector collaboration among leasing companies, car dealerships, and regulatory bodies to ensure broad accessibility and effective learning. The findings contribute to developing scalable, inclusive, and safety-oriented frameworks for driver education in emerging vehicle technologies.

Urban Air Mobility (UAM) is an emerging transportation solution aiming to alleviate congestion and enhance sustainability in urban areas. Automation capabilities are evolving from manned control to full autonomy, and at each stage of this progression, the willingness to use each system represents a behavioural intention toward adoption. This study investigates willingness to use UAM for airport shuttle services across three levels of automation: Manned control, Remotely piloted, and Fully autonomous. The study employed survey data from 1613 respondents in South Korea. Using ordered logit models, we examine how socio-demographic and current airport travel behaviour influence adoption. Results show that approximately 60% of respondents were willing to use manned control UAM, whereas only about 30% expressed willingness under remotely piloted or fully autonomous UAM. Adoption patterns and predictors of adoption vary depending on the level of automation. While current travel time to the airport and the primary purpose of airport use are prominent predictors of willingness to use UAM at the manned control level, demographic characteristics, particularly gender and age, become more significant at remotely piloted and fully autonomous UAM, as the level of automation increases. The frequency of international travel consistently predicts a higher willingness to adopt UAM, regardless of the level of automation. These findings highlight the need for level-specific adoption strategies and suggest that trust and risk perception need to be addressed as automation increases. This study contributes empirical evidence for policymakers, service operators, and urban planners informing differentiated communication and integration strategies tailored to user profiles and system maturity.

In automated vehicles, beeps are widely used as alarms and feedback. However, as automation advances, there is a need to explore subtler, contextually sound-based notifications for non-urgent situations. While auditory interfaces for take-over requests have been studied, limited attention has been given to using soundscapes for such alerts. This paper designed and evaluated soundscapes using existing driving-related sounds–amplified road noise and/or dimmed background music–for scheduled take-over situations. A driving simulator study showed that these soundscapes enhanced reaction time, situation awareness, and acceptance without causing annoyance. Particularly, the combined condition (music dimming and road noise amplifying) supported higher driver awareness and responsiveness. These findings suggest that soundscapes can offer safer, more intuitive take-over alerts by embedding information into familiar audio cues. This study contributes to developing soundscapes as novel alert mechanisms that integrate seamlessly with the driving environment to enhance both safety and user experience in automated vehicles.

As vehicles transition between driving automation levels, drivers need to be continually aware of the automation mode and the resulting driver responsibilities. This study investigates the impact of visual user interfaces (UIs) on drivers’ mode awareness in SAE Level 2 automated vehicles. It focuses on their understanding of speed and distance control, steering control, and the hands-on steering wheel requirement presented through UIs. Forty-five UIs were generated, presenting the activation of Lane Keeping Assist (LKA) and Adaptive Cruise Control (ACC) and the hands-on steering wheel requirement. Through an online questionnaire with 1080 respondents with experience of SAE Level 2, the study evaluated how these visual UIs influenced users’ understanding of control responsibilities, information usability, and trust in automated vehicles. The results show a limited role of UI in shaping users’ understanding of control. ACC UIs and LKA UIs had no significant effects, and apparently, the understanding of speed and distance control and steering control was independent of the ACC UI and LKA UI. A large variance in responses regarding the understanding of steering control and speed and distance control indicates confusion caused by mode ambiguity, suggesting that drivers do not well understand how the speed and distance control and steering control task is shared between the driver and the automation. However, the hands-on steering wheel UIs significantly improved the understanding of the hands-on steering wheel requirement. The hands-on steering wheel UI combining the hands on the wheel icon and the text “Keep hands on steering wheel” yielded 94.4% correct understanding and outperformed the UI with hands but without text (87.8% correct) or no UI (82.5% correct). In addition, the variation of visual UI did not affect trust. This study contributes to the understanding and design of visual UIs for effective communication of driver responsibilities in automated vehicles.

The integration of Advanced Driver Assistance Systems (ADAS) in vehicles marks an advancement in automotive safety and driving efficiency. However, to obtain the benefits of ADAS, drivers need to understand and utilise the systems properly. This study investigates the strategies employed by U.S. drivers to learn about and operate ADAS, focusing on both the learning channels through which they acquire information and the learning content of that information. Through in-depth interviews with twenty-three drivers, who are experienced with SAE Level 2 partial driving automation, this study explores the learning methods, experiences, and knowledge drivers gain when interacting with ADAS features. The findings show that trial-and-error is the predominant method drivers use to learn about ADAS. In addition, dealerships are critical points for providing initial information, but there is variability in the quality and timing of information provided by sales personnel. In the initial learning phase, it is important to convey critical information, such as the basic functionality, operation, and limitations, while avoiding information overload. In addition, drivers expressed a desire to receive feedback on the status of these features and the reasons for any occurrences while driving. The study highlights the necessity of comprehensive learning strategies incorporating multiple learning channels, including driving schools, dealerships, digital resources, and practical experience through trial-and-error. This research shows the importance of a structured and adaptable approach to ADAS learning, tailored to drivers’ preferences and behaviours, to maximise the safety and utility of driving automation.

As vehicles with several levels of automation become increasingly common, there is an increase in incidents involving the misuse of Driving Automation Systems (DAS). The manner in which drivers interact with DAS indicates that the problem extends beyond UI design. We investigate how drivers' perceptions and expectations affect the understanding and consequent usage of DAS. The study employed a Wizard-of-Oz approach to simulate a vehicle with a Level 2 and Level 3 DAS on a public highway. Sixteen participants were exposed to the two driving modes and two distinct UIs. Observations, think-aloud protocols, and in-depth interviews documented their interaction with the different DAS. Irrespective of the UI, various errors were detected, including omission, commission, and mode confusion. Deeper investigation into the sources led to the conclusion that drivers' preconceptions of the DAS were a major contributor, resulting in misuse. This highlights the need to look beyond UI design as a sole solution to address driver-vehicle interaction.

With the increasing adoption of driving automation technologies, vehicles equipped with SAE Level 3 driving automation are becoming available on the market. This study explores drivers’ behaviour when driving conditionally automated vehicles on-road, providing multiple levels of driving automation. Sixteen participants drove a Wizard-of-Oz vehicle offering several levels of automation (Manual, SAE Level 2 and Level 3) on a public highway. Data was collected during driving sessions (observations and think-aloud) and post-driving sessions (in-depth interviews). The results indicate that drivers show errors in mode transitions and mode awareness. These errors include unintended deactivation of Level 2 driving automation, confusion about driving modes after disengaging Level 3 driving automation, and confusion about the current driving mode. These findings highlight a fundamental limitation in the design of automation systems when humans are required to operate multiple modes within a single system, making it challenging to distinguish between them clearly. This ambiguity and lack of understanding affected how drivers interacted with, interpreted, and responded to the automated vehicle. The study provides insights for designing automated vehicles with multiple levels of driving automation, aiming to improve mode awareness and overall safety.

Introduction: The advancement of advanced driver assistance systems (ADAS) aims to enhance driving safety, efficiency, and convenience. However, their potential remains underutilized as drivers frequently disengage or avoid using these systems. This study investigates the phenomenon of ADAS disuse, encompassing situational disengagement and systematic avoidance, through in-depth interviews with SAE Level 2 automated vehicle drivers. Method: Using thematic analysis, we identified nine key themes influencing disuse across three domains: Driving task (strategic, tactical, and operational level of driving tasks); Human (sense of control, knowledge, trust, and responsibility); and Environment (road users and road situation). Results: Drivers cited discomfort with system aggressiveness, lack of trust in detection capabilities, and incompatibility with their driving styles as critical factors. Environmental complexities, such as construction zones and pedestrian-heavy areas, further exacerbated disengagement. Additionally, legal and moral responsibility emerged as influences on drivers’ preferences for manual control. Conclusions: Our findings underscore the need for adaptive, user-centered designs prioritizing trust, transparency, and context-sensitive system behaviors. By addressing these barriers, ADAS can achieve safer and more consistent adoption, supporting broader goals of accident prevention and traffic efficiency. Practical Applications: This study provides insights for enhancing ADAS design and fostering driver confidence, paving the way for their effective integration into modern mobility solutions.

Mode awareness is important for the safe use of automated vehicles, yet drivers' understanding of mode transitions has not been sufficiently investigated. In this study, we administered an online survey to 838 respondents to examine their understanding of control responsibilities in partial and conditional driving automation with four types of interventions (brake pedal, steering wheel, gas pedal, and take-over request). Results show that most drivers understand that they are responsible for speed and distance control after brake pedal interventions and steering control after steering wheel interventions. However, drivers have mixed responses regarding the responsibility for speed and distance control after steering wheel interventions and the responsibility for steering control after gas pedal interventions. With a higher automation level (conditional driving automation), drivers expect automation to remain responsible more often compared to a lower automation level (partial driving automation). Regarding Hands-on requirements, more than 99% of respondents answered that drivers would keep their hands on the steering wheel after all intervention types in partial automation, while 60–95% would place their hands on the wheel after various intervention types in conditional automation. A misalignment between actual logic and drivers' expectations regarding control responsibilities is observed by comparing survey responses to the mode transition logic of commercial partially automated vehicles. To resolve confusion about control responsibilities and ensure consistent expectations, we propose implementing a consistent mode design and providing enhanced information to drivers.

The emergence of automated vehicles promises a revolution in urban mobility. To benefit from a new mobility system, women who have specific mobility considerations necessitate inclusion in designing automated vehicles. This study explores women's perspectives and the potential impact of automated vehicles through focus group discussions and in-depth interviews. Results demonstrate concerns among women about safety in current mobility systems, vulnerabilities regarding personal safety, and stereotypes about female drivers. Additionally, mothers face additional challenges managing items for children and their demands during travel, and senior women consider safety issues and declining capabilities when contemplating driving cessation. Current experience with mobility is reflected in concerns and visions regarding automated vehicles. The absence of a driver is expressed as improved safety in driverless taxis, while it is perceived as a safety concern in automated public transportation. Mothers with children anticipate convenience in travel, whereas senior women expect enhanced mobility and social participation. These findings underscore the importance of safety in women's mobility experiences and provide insights into addressing safety and interaction issues in the design of automated vehicles. Researchers, transportation authorities, and vehicle manufacturers can leverage these results to understand women's needs better and consider them in future designs and policy developments for automated vehicles. Prioritising women's perspectives in automated vehicle research is essential to realising the innovative potential of this technology and fostering a more inclusive and accessible future in urban mobility.

As the development of Generative AI technology continues to progress, the opportunity for innovation with AI in the form of user interfaces, products and services within vehicles is expanding. Furthermore, automobiles are undergoing major transformations in design due to changes in the underlying technology resulting in evolved user needs, behaviors, activities and aspirations. This workshop is aimed at providing the participants hands-on experience of designing novel Generative AI interfaces for vehicles. While working on the design challenge as the connecting thread, we will introduce and weave together modules of knowledge domains focusing on Human-centered design, Ethical and Responsible behavior, and Autonomy in vehicles. Participants will learn about and engage collaboratively in employing design methods such as Co-creation using Activity Canvases, Enactment, Wizard of Oz, Bodystorming and inter-group discussion. As the outcome, we aim to publish participant’s design concepts as a booklet and a research paper, and seek new research collaborations.

Automated vehicles are anticipated to not only improve safety and comfort but also redefine the nature of driver-vehicle interactions. With these advancements, drivers in automated vehicles share the monitoring and supervising role with the system, creating a unique human-machine team. In this relation, beyond technical excellence, it is important for drivers to comprehend and trust the system for its effective and safe utilisation. Trust evolves during interactions, influenced by the system’s performance, emphasising the significance of provided information and interaction format. Design features of driving automation, particularly user interfaces, impact trust and shape perceptions of system performance, forming a crucial element for human-machine interaction.

This dissertation addresses three critical research gaps. First, an understanding of driver-vehicle interaction is crucial for designing effective interfaces. Current studies lack a clear comprehension of interactions during driving and transitions in complex systems, emphasising the need for an integrated view that considers various contexts and levels of driving automation. In this dissertation, I address this gap by investigating how drivers perceive complex interactions and identifying necessary interactions for drivers. Second, interactions extend beyond isolated take-over events, forming a sequence of interconnected behaviours that shape the overall driving experience. While take-over situations are undeniably critical, they represent just one facet of a broader continuum of scenarios. In-depth research on interactions during automated driving, where the human driver transitions between passive monitoring and active engagement, is lacking. Therefore, I explore the interactions in various situations, including automated driving and mode transitions in automated vehicles. Third, my research emphasises the design phase of user interfaces, beyond the conventional focus on identifying the effects of interfaces on drivers in specific scenarios. It stresses the importance of considering interface design comprehensively, incorporating factors such as cognitive load, situational awareness, and driver experience. Specifically, I look into the design of soundscapes to guide the driver back to control in take-over situations, creating a novel transition experience while prioritising safety.

Aligned with the goal of designing human-machine interaction for trustworthy automated vehicles, the research objectives are delineated into two objectives and six studies. The first objective aims to understand the driver-automated vehicle interaction, focusing on identifying the effects of interfaces, investigating human-machine interaction, and understanding drivers’ mode transition logic. The second objective aims to contribute to the development of interaction design guidelines, designing and evaluating user interfaces and developing an approach to soundscape design in automated vehicles. Finally, I consolidate the findings, discuss the research’s contribution, and provide an outlook on future work in the evolving landscape of automated vehicle technology.

Six studies employing diverse methods yield following results. A literature review and an on-road study unveil that the interaction between the driver and the automated vehicle through user interfaces significantly influences drivers’ performance and trust. Specifically, the literature review indicates that this interaction during automated driving has an impact extending from take-over situations to overall performance. The on-road study, using a vehicle with multiple levels of driving automation, reveals mode confusion related to mode transitions. Further exploration of driver’s understanding of mode transition logic, through an online survey demonstrates that, despite the driver performing certain interventions related to driving functions, there is no dominant mental representation of mode-transition logic under specific scenarios. Simulator experiments in partial and conditional automated driving scenarios illustrate that providing automation information during driving enhances the driver’s trust and acceptance. In particular, delivering the manoeuvre of driving automation to the driver through auditory modality is effective in enhancing trust and acceptance. The proposed soundscape design for take-over requests and spatial sound delivering manoeuvre information while automated driving showcases the potential of sound design to elevate the driver’s experience beyond a simple beep.

Throughout this dissertation, I investigate interactions in automated vehicles, addressing interaction challenges and designing and evaluating user interfaces. By conducting a literature review, on-road observations, interviews, online surveys, and simulator experiments, it navigates the complexities of trust, acceptance, and overall user experience. The contributions extend to understanding driver trust, performance, and the impact of system complexities on human-machine interaction, enriching the field with empirical evidence and practical guidelines for interaction design.

In conditionally automated driving (SAE level 3), drivers may take their eyes off the road but will still need to be ready to take control and will, therefore, benefit from information on automation. This study aims to investigate the effectiveness of automation manoeuvre information provided through spatial sound, traditional notification sound (beep), and a visual interface. Spatial sounds were designed differentiating four distinct driving manoeuvres: overtaking a leading car, slowing down, turning right, and passing a roundabout. The notification sound consisted of one beep being identical for all manoeuvres. The visual interface showed the automation mode with an image and manoeuvre information with text and images. The impact of these interfaces on trust, workload, acceptance, situation awareness, and sense of control was evaluated with questionnaires and visual attention was evaluated with eye tracking while participants engaged in a visual-motor secondary task in a driving simulator. The results indicate that, with all interfaces tested, manoeuvre information enhances trust, acceptance, situation awareness, and sense of control, without significantly affecting the overall workload. These benefits were more profound, adding auditory information and differed marginally between the traditional notification and the spatial sound, as the effectiveness of the different auditory interface types varied depending on the specific manoeuvre. Findings highlight the importance of designing user interfaces for automation manoeuvre information using auditory cues to improve the user experience in automated driving.

Trust and perceived safety are pivotal in the acceptance of automated vehicles and can be enhanced by providing users with automation information on the (safe) operation of the vehicle. This study aims to identify how user interfaces (UI) can enhance drivers' trust and acceptance and reduce perceived risk in partially automated vehicles. Four interfaces were designed with different levels of complexity. These levels were achieved by combining automation information (surrounding information vs surrounding and manoeuvre information) and modality (visual vs visual and auditory). These interfaces were evaluated in a driving simulator in which a partially automated vehicle reacted to an event of a merging and braking vehicle in its front. The criticality of the events was manipulated by the factors merging gap (in meters) and deceleration (m/s²) of the vehicle in front. The reaction of the automation was either to brake or to change lanes. The results show that an optimal combination of automation information and modality enhances drivers' trust and acceptance. More specifically, the most advanced UI, which provided surrounding and manoeuvre information via the visual and auditory modalities, was associated with the highest trust and acceptance ranking and the lowest perceived risk. Manoeuvre information delivered through the auditory modality was particularly effective in enhancing trust and acceptance. The benefits of the UIs were consistent over events. However, in the most critical events, drivers did not feel entirely safe and did not trust the automation completely. This study suggests that the design of UIs for partially automated vehicles shall include automation information via visual and auditory modalities.

In the future, automated vehicles can allow much longer journeys to be undertaken without a driver. This could involve travel across countries with different road systems, traffic rules, and regulations. Disparate cultures also have different expectations of how vehicles should behave. These need to be taken into account when designing HMIs to convey vehicle behaviour and intent. In this work, we presented four scenarios where culturally-sensitive HMIs could benefit both drivers and other road users in a cross-cultural setting. This video will hopefully provoke further discussion and research interest in cross-culture human-vehicle interaction.

As the adoption of automated vehicles becomes more prevalent, there is a need for systematic approaches to understanding, communicating, and analyzing the design aspects of the technology. Designing a taxonomy - a hierarchical framework that classifies and organizes design elements and features - is a critical tool in this pursuit. A well-defined taxonomy provides an effective communication tool and enables stakeholders to understand, discuss, and compare system designs. However, current taxonomies often fail to consider technological advancements and the needs of stakeholders. This gap limits the understanding of vehicle automation and hinders addressing critical aspects such as safety and social impact. This workshop aims to propose a stakeholder-centered taxonomy for automated vehicles, taking into consideration the perspectives and requirements of different stakeholder groups, such as technology professionals, policymakers, and end-users. The workshop aims to identify relevant considerations for taxonomy design that cater to different stakeholders, incorporate diverse perspectives, and highlight opportunities and challenges for effective communication and decision-making in vehicle automation.

Automation of vehicles not only provides greater safety, but also many previously unimagined opportunities, such as less inequality, less stress, and more meaningful activities while driving. However, the uptake and implementation of automated driving have been falling short of its promise, due to challenges in identifying safe and acceptable ways for humans to interact with automated vehicles. The human-machine interface (HMI) in vehicles plays a more critical role today than ever before. The main research question addrssed in this study, as part of the EU-funded HADRIAN project, is: What steps need to be taken to achieve effective fluid HMI (f-HMI) design for improved driver-vehicle dialogue, especially in critical scenarios?.

Introduction: SAE Level 3 is known as conditional driving automation. As long as certain conditions are met, there is no need to supervise the technology and the driver can engage in non-driving related tasks (NDRTs). However, a human driver must be present and alert to take over when the automation is facing its system limits. When such an emergency takes place, the automation uses the human machine interface (HMI) to send a take-over request (TOR) to the driver.

Methods: We investigated the influence of a color themed HMI on the trust and take-over performance in automated vehicles. Using a driving simulator, we tested 45 participants divided in three groups with a baseline auditory HMI and two advanced color themed HMIs consisting of a display and ambient lighting with the colors red and blue. Trust in automation was assessed using questionnaires while take-over performance was assessed through response time and success rate.

Results: Compared to the baseline HMI, the color themed HMI is more trustworthy, and participants understood their driving tasks better. Results show that the color themed HMI is perceived as more pleasant compared to the baseline HMI and leads to shorter reaction times. Red ambient lighting is seen as more urging than blue, but HMI color did not significantly affect the general HMI perception and TOR performance.

Discussion: Further research can explore the use of color and other modalities to express varying urgency levels and validate findings in complex on road driving conditions.

The design of take-over requests in automated vehicles traditionally focuses on safety and reaction time. We are interested in how take-over requests can be designed from a broader user experience perspective while ensuring safety. This paper proposes designs for ambient sound (i.e., soundscape) and driving noise to inform the driver of transition situations. Drivers must take-over control within the time budget, the time from the take-over request to the automation system limit. The time required for a safe transition depends on the complexity of the driving environment. In a scheduled take-over, which is not an emergency, there is an opportunity for an interaction that gradually introduces the driver into the transition process. Ambient sound is expected to lead the driver back to the loop with comfort, creating a novel transition experience as well as safety.