Advanced driver assistance systems (ADAS) with increasing automation maturity and availability in urban contexts are entering the market. Meanwhile, the situational context has been identified to play a crucial role in system comprehension and usage, yet its subcomponents and their relation to system comprehension remain an open research question. To gain insights in the role of the situation complexity regarding subjective system comprehension and different methodological aspects, this study applies a mixed quantitative and qualitative approach, focusing on signaled intersections as an exemplary scenario. An on-road study with forty-six participants was conducted, involving six traffic light scenarios (all experienced twice). Results indicate that while comprehension was generally high, the situational context, including environmental and traffic-related factors, affected subjective system understanding. The proposed approach sheds light on the role of mixed methods in ADAS research, which may provide insights for system developers and suggestions for user training content.
We demonstrate a web app user interface that simulates the benefits and trade-offs of Vehicle-to-Grid (V2G) given an individual’s electric vehicle driving habits. Users input their own driving and charging data (or explore example usage patterns), specify their battery type, and indicate their availability for V2G. The interface then generates a personalized V2G schedule and presents both the potential financial benefits (e.g., earnings from supplying energy to the grid) and impact on battery health. This interactive and personalized interface aims to mitigate known barriers to V2G participation and increase consumer interest in the technology.
Scenarios provide a fundamental link between driving simulators and real-world conditions, shaping the extent to which the findings of a user study can be applied to public roads. However, compared to other aspects of study design, scenario development in human-vehicle interaction research tends to receive less deliberate attention. To encourage more methodical scenario generation, this work introduces a mixed methods approach for extracting representative scenarios from an integration of three real-world data sources: aggregated crash statistics, interviews with experienced drivers, and naturalistic driving data. Through a case study on winter driving, we outline the derivation of a nighttime, two-lane road scenario from these data sources and conduct an initial driving simulator pilot study to assess its realism. We hope that this demonstration of scenario generation from quantitative and qualitative data inspires researchers to consider more rigorous methods for scenario design in future work.
Affording users the opportunity to customize automated vehicle (AV) behavior may meaningfully improve trust and acceptance. We examined this in a driving simulator study with 49 students and 18 community members (N = 67), randomly assigned to customization or non-customization conditions. Customization participants selected from a range of behavior options and experienced one of three driving styles. Results showed that the effect of customization on trust depended on the driving style. Specifically, those who customized and received the conservative style reported significantly higher trust than those who passively received it. Exploratory analyses using the Unified Theory of Acceptance and Use of Technology (UTAUT) revealed that AV interest explained 8% additional variance in behavioral intentions beyond UTAUT attitude variables. These findings support prior work emphasizing trust in AV adoption and extend it by positioning trust as a potential distal predictor of behavioral intentions, acting through performance and effort expectancy.
Modern vehicle infotainment systems are increasingly multimodal, enabling drivers to interact through voice, touch, and gesture. While these input methods enhance usability, most existing tools overlook the dynamic driving context-such as traffic density, road complexity, and cognitive load, leading to incomplete evaluations. This work-in-progress introduces the Context-Aware Usability Scale (CAUS), a psychometric instrument for evaluating multimodal in-vehicle interfaces with sensitivity to environmental factors. CAUS is developed through a three-phase process: (1) item generation from a systematic literature review using the PRISMA framework, (2) expert validation via Content Validity Index (CVI) methods, and (3) planned empirical testing in a high-fidelity driving simulator. The 20-item scale spans five core usability dimensions - efficiency, effectiveness, cognitive load, distraction, and satisfaction - and five context-sensitive items. Expert evaluations confirmed strong content validity (S-CVI > 0.90). The final phase will assess reliability and factor structure. CAUS offers an ecologically valid, scalable tool for evaluating automotive HCI, with applications in smart mobility and mobile interface design.
