Automated Driving: The Potential of Non-driving-Related Tasks to Manage Driver Drowsiness

Author(s):  
Veronika Weinbeer ◽  
Tobias Muhr ◽  
Klaus Bengler
2018 ◽  
Vol 1 (3) ◽  
pp. 99-106 ◽  
Author(s):  
Ryuichi Umeno ◽  
Makoto Itoh ◽  
Satoshi Kitazaki

Purpose Level 3 automated driving, which has been defined by the Society of Automotive Engineers, may cause driver drowsiness or lack of situation awareness, which can make it difficult for the driver to recognize where he/she is. Therefore, the purpose of this study was to conduct an experimental study with a driving simulator to investigate whether automated driving affects the driver’s own localization compared to manual driving. Design/methodology/approach Seventeen drivers were divided into the automated operation group and manual operation group. Drivers in each group were instructed to travel along the expressway and proceed to the specified destinations. The automated operation group was forced to select a course after receiving a Request to Intervene (RtI) from an automated driving system. Findings A driver who used the automated operation system tended to not take over the driving operation correctly when a lane change is immediately required after the RtI. Originality/value This is a fundamental research that examined how the automated driving operation affects the driver's own localization. The experimental results suggest that it is not enough to simply issue an RtI, and it is necessary to tell the driver what kind of circumstances he/she is in and what they should do next through the HMI. This conclusion can be taken into consideration for engineers who design automatic driving vehicles.


2019 ◽  
Vol 70 (3) ◽  
pp. 184-192
Author(s):  
Toan Dao Thanh ◽  
Vo Thien Linh

In this article, a system to detect driver drowsiness and distraction based on image sensing technique is created. With a camera used to observe the face of driver, the image processing system embedded in the Raspberry Pi 3 Kit will generate a warning sound when the driver shows drowsiness based on the eye-closed state or a yawn. To detect the closed eye state, we use the ratio of the distance between the eyelids and the ratio of the distance between the upper lip and the lower lip when yawning. A trained data set to extract 68 facial features and “frontal face detectors” in Dlib are utilized to determine the eyes and mouth positions needed to carry out identification. Experimental data from the tests of the system on Vietnamese volunteers in our University laboratory show that the system can detect at realtime the common driver states of “Normal”, “Close eyes”, “Yawn” or “Distraction”


GIS Business ◽  
2016 ◽  
Vol 11 (6) ◽  
pp. 14-16
Author(s):  
Eicher, A

Automated driving: The person continues to decide Automatisiertes Fahren: Der Mensch entscheidet weiterhin


Sign in / Sign up

Export Citation Format

Share Document