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.

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.

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.