Measuring Interaction-based Secondary Task Load: A Large-Scale Approach using Real-World Driving Data

TL;DR

This paper proposes a model-based approach to estimate driver workload from interaction data with vehicle touchscreens, aiming to improve safety assessments early in the design process using real-world driving data.

Contribution

It introduces a novel method combining user interactions and UI elements to predict secondary task load, leveraging large-scale natural driving data.

Findings

Preliminary results show potential for workload prediction accuracy.

The approach can be integrated into early design stages.

Using real-world data enhances model relevance and robustness.

Abstract

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.