Examining the Effect of Collaboration Effort, Input Method, and Age on Driver–Automation Collaboration in Unstructured Driving Environments

Abstract

Both drivers and the current state of advanced driver-assistance systems (ADASs) are imperfect, but their collaboration as a team can compensate for individual limitations. Unstructured driving environments (e.g., parking lots and off-road trails) entail greater challenges for an ADAS and require driver–automation collaboration. However, few studies describe the factors shaping drivers’ subjective experience of collaboration with driving automation in unstructured environments. In this study, we examined the effects of collaboration effort and input method on drivers’ subjective experience, including mental workload, trust, and perceived usability. In addition, we examined how collaboration is experienced differently across different age groups. In this driving simulation study, participants collaborated with a hypothetical ADAS to navigate two unstructured driving environments. The results indicate that collaboration effort and input method did not directly affect drivers’ collaboration experience. However, examining the effect of collaboration effort by driver age suggests that older drivers (55 years old and over) experienced higher mental workload and gave lower usability ratings to the collaboration method than younger drivers. These results imply that older drivers may experience more challenging collaboration than younger drivers when they must devote substantial cognitive effort to collaborate with automation. The findings suggest designing driver–automation collaboration features to reduce cognitive effort for older drivers navigating unstructured driving environments. Moreover, future research must examine drivers’ collaboration experiences based on their unique situations, shaped by the interplay between situational properties (e.g., task demands) and driver characteristics (e.g., age) rather than relying on a limited understanding of individual factors.