Presentation
A Methodological Framework for Evaluating Longitudinal Vehicle Control Features
SessionPoster Session 1
DescriptionAdvanced Driver Assistance Systems (ADAS) are designed to support drivers by managing tasks like speed and braking, but their effectiveness relies heavily on drivers understanding how the systems work—especially when features overlap. This study presents a framework for analyzing confusion that can arise when drivers interact with multiple longitudinal assist features, such as Adaptive Cruise Control and Traffic Jam Assist. Using state diagrams and real-world driving data, the research identifies moments where system transitions may be unclear, leading to hesitation or misuse. We conducted on-road studies with drivers and used think-aloud protocols, and pedal monitoring to capture how drivers perceive system status and control. By combining Cognitive Task Analysis (CTA) with behavioral modeling, the framework provides insights into how mental models influence driver decisions and system usability. The results will highlight key areas of confusion and suggest ways to improve interface design and automation logic. This work helps ensure that automation supports drivers effectively, rather than introducing new challenges.
Event Type
Poster
TimeTuesday, October 14th5:30pm - 6:30pm CDT
LocationRiverside East
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