Claus Marberger

Understanding and Applying the Concept of “Driver Availability” in Automated Driving

Several levels of automated driving functions require the human as a fallback driver in case system performance limits are exceeded. Human factors research in this area is especially concerned with human performance in these take-over situations and the influence of the driver state. Based on work of the publicly funded project Ko-HAF the paper introduces a comprehensive model of the transition process from automated driving to manual driving and specifies relevant time stamps and time windows. The concept of Driver Availability is regarded as a quantitative measure that relates the estimated time required to safely take-over manual control to the available time budget. A conceptual framework outlines potential influencing factors on driver availability as well as ways to apply the measure in a real-time application.

Introducing WESAD, a Multimodal Dataset for Wearable Stress and Affect Detection

Affect recognition aims to detect a persons affective state based on observables, with the goal to e.g. improve human-computer interaction. Long-term stress is known to have severe implications on wellbeing, which call for continuous and automated stress monitoring systems. However, the affective computing community lacks commonly used standard datasets for wearable stress detection which a) provide multimodal high-quality data, and b) include multiple affective states. Therefore, we introduce WESAD, a new publicly available dataset for wearable stress and affect detection. This multimodal dataset features physiological and motion data, recorded from both a wrist- and a chest-worn device, of 15 subjects during a lab study. The following sensor modalities are included: blood volume pulse, electrocardiogram, electrodermal activity, electromyogram, respiration, body temperature, and three-axis acceleration. Moreover, the dataset bridges the gap between previous lab studies on stress and emotions, by containing three different affective states (neutral, stress, amusement). In addition, self-reports of the subjects, which were obtained using several established questionnaires, are contained in the dataset. Furthermore, a benchmark is created on the dataset, using well-known features and standard machine learning methods. Considering the three-class classification problem ( baseline vs. stress vs. amusement ), we achieved classification accuracies of up to 80%,. In the binary case ( stress vs. non-stress ), accuracies of up to 93%, were reached. Finally, we provide a detailed analysis and comparison of the two device locations ( chest vs. wrist ) as well as the different sensor modalities.