Many drivers misjudge what their vehicle’s automation systems can actually do. This mismatch, known as mode confusion, can turn small misunderstandings into fatal consequences. Research has long examined drivers’ mental models and drivers’ confidence in engaging Advanced Driver Assistance Systems (ADAS), treating both as key contributors to mode confusion. Yet one crucial question remains largely unaddressed: do drivers know, correctly and confidently, which automation features are installed in their own vehicles? To address this question, we surveyed 1,487 U.S. vehicle owners whose manufacturers list Adaptive Cruise Control (ACC) and Lane Keeping Assistance (LKA) as standard equipment. Each respondent’s self-reported ownership awareness was compared with external model-trim data. Despite generally high ownership confidence, 17.1% incorrectly believed their vehicle lacked ACC and 29.4% believed it lacked LKA. Ownership awareness is uneven across ADAS: LKA is misjudged more often than ACC, even among drivers who are confident in their ownership judgments. Specifically, owning an older vehicle is associated with lower ownership accuracy and lower ownership confidence, while exposure to demanding trip contexts is more strongly related to lower ownership confidence than to lower ownership accuracy. Analyses of self-reported reasons using Holm-adjusted Fisher tests and association-rule mining reveal why ownership-awareness misalignment occurs. Misaligned ownership awareness commonly co-occurs with a lack of information and a lack of first-use experience, often coupled with an acceptance barrier that may reflect reluctance to engage initially with ADAS. In contrast, correct-and-confident ownership awareness co-occurs with prior ADAS use, clear in-vehicle feedback, and dealer explanation. Taken together, our findings suggest opportunities to help mitigate early mode confusion, including enhancing feedback and status visibility in in-vehicle interfaces and supporting guided first use through sales interactions or in-vehicle onboarding experiences, both of which warrant further testing.

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.

Advanced Driver Assistance Systems (ADAS) promise to enhance road safety, reduce driver workload, and improve driving comfort. However, their real-world impact is shaped by how drivers adapt their behaviour over time. This study explores the behavioural adaptations associated with the use of ADAS through qualitative analysis of interviews with sixty drivers in Australia. As a result, four interrelated themes emerge: 1. Degradation of driving skills , 2. Shifting risk perception and tolerance with ADAS , 3. Reduced cognitive and physical engagement in driving tasks , and 4. Adaptation to system warnings . While ADAS can support safer driving, the findings reveal that over-reliance and complacency are common, potentially undermining the intended safety benefits. We argue that these behavioural adaptations form a dynamic process shaped by trust, perceived system capabilities, and user habits. To address this, integrated strategies that combine adaptive interface design, regulatory oversight, driver training, and real-time monitoring are needed to sustain safety and user competence. This study contributes insights into the emerging behavioural consequences of ADAS adoption in everyday contexts.

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.

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.

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.

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.

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.

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.

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 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.