Measuring Interaction-based Secondary Task Load: A Large-Scale Approach using Real-World Driving Data
This work addresses the need for early workload estimation in automotive HMI development to reduce reliance on expensive prototypes, though it appears incremental as it builds on existing model-based methods.
The paper tackles the problem of estimating driver workload when interacting with vehicle center touchscreens, proposing a model-based approach using large-scale real-world driving data to predict secondary task load early in development, with preliminary results indicating potential for cost and time savings.
Center touchscreens are the main HMI (Human-Machine Interface) between the driver and the vehicle. They are becoming, larger, increasingly complex and replace functions that could previously be controlled using haptic interfaces. To ensure that touchscreen HMI can be operated safely, they are subject to strict regulations and elaborate test protocols. Those methods and user trials require fully functional prototypes and are expensive and time-consuming. Therefore it is desirable to estimate the workload of specific interfaces or interaction sequences as early as possible in the development process. To address this problem, we envision a model-based approach that, based on the combination of user interactions and UI elements, can predict the secondary task load of the driver when interacting with the center screen. In this work, we present our current status, preliminary results, and our vision for a model-based system build upon large-scale natural driving data.