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.

Drivers often misjudge the capabilities of Advanced Driver Assistance Systems (ADAS), compromising safety. Guided by a Context–Vehicle–Driver (C-V-D) framework drawn from 22 empirical studies, this study analyzed a secondary survey of 838 drivers to identify predictors of self-reported “ADAS unawareness” (“I don’t know if I use it”). Analysis of the representation ratio (RR) showed that drivers with a low annual driving distance (' 5000 km), lack of private car ownership, and young age (18–29 years) were consistently overrepresented among unaware users (RR ≥ 1.2), while car sharing frequency and license tenure were not. Un-awareness was highest for Adaptive Cruise Control (ACC) among the three ADAS examined. These results support a hierarchical account in which contextual factors outweigh vehicle and driver-level influences. The C-V-D model yields testable hypotheses for road type, traffic density, and interface design that merit evaluation in larger-sample studies. Addressing the priority groups identified here can help designers, dealers, and educators reduce mode confusion and promote safe ADAS adoption.

Driving automation holds significant potential for enhancing traffic safety. However, effectively handling interactions with human drivers in mixed traffic remains a challenging task. Several models exist that attempt to capture human behavior in traffic interactions, often focusing on gap acceptance. However, it is not clear how models of an individual driver's gap acceptance can be translated to dynamic interactions between humans and automated vehicles (AVs) in the context of high-speed scenarios like overtaking. In this study, we address this issue by employing a cognitive process modeling approach. We investigate a variety of drift-diffusion models to describe the dynamic decision-making process of the driver during overtaking maneuvers. Our findings reveal that a drift-diffusion model incorporating an initial decision-making bias dependent on the initial velocity can accurately describe the qualitative patterns of overtaking gap acceptance observed previously. Our results demonstrate the potential of the cognitive process approach in modeling human overtaking behavior when the oncoming vehicle is an AV. To this end, this study contributes to the development of effective strategies for ensuring safe and efficient overtaking interactions between human drivers and AVs.