Drowsiness Detection and Warning in Manual and Automated Driving: Results from Subjective Evaluation

Automated Driving Interface & Takeover DesignIn-Vehicle Haptic, Audio & Multimodal FeedbackAutomotive Manufacturers & Vehicle DesignersAutonomous Driving Engineers & Test DriversPedestrians & Vulnerable Road Users

Drowsiness is a main cause of serious traffic accidents, and problematic within the ongoing automation of the driving task. Several approaches for drowsiness detection have been published and are in operation in production cars for manual driving. To assess differences in the development of drowsiness between manual and automated driving, and to further investigate the potential of subjective ratings, we conducted a driving simulator study (N=30). The self-assessment was based on the Karolinska Sleepiness Scale (KSS), during and after driving. Furthermore, we examined the impact of travel time and driver age (20-25, 65-70 years). Results confirm that driving mode and travel time have a significant effect on the development of drowsiness. In both age groups, self-ratings were higher for automated driving and particularly by younger subjects. All subjects estimated themselves drowsier during driving. The gained knowledge can be helpful for the development of future driver-vehicle interfaces in driver drowsiness detection.

Quick Actions

Share

Share this page

ios_share

https://hci.top/en/papers/auto_ui/7485/2018

AdRecommended

Learn AI Coding at CodeNow

At a Glance

Paper Snapshot

fact_check
dataset
Source
AutoUI
calendar_month
Year
2018
emoji_events
Award
No award tagged
group
Authors
4 authors
sell
Subtopics
Automated Driving Interface & Takeover Design, In-Vehicle Haptic, Audio & Multimodal Feedback
work
Professions
Automotive Manufacturers & Vehicle Designers, Autonomous Driving Engineers & Test Drivers, Pedestrians & Vulnerable Road Users
article
Content Status
Abstract only
hub
Related Papers
10 related papers