The estimation of drowsiness under different conditions based on behaviour observations from a simulator study Drowsiness problem
- Drowsiness is an intermediate condition between alertness and
sleep which reduces the consciousness level and hinder a person to respond quickly (Awais, Badruddin, & Drieberg, 2017)
- Drowsy driving has caused about 2.5 percent of fatal accidents
from 2011 through 2015 in the USA (Correa, Orosco, & Laciar, 2014)
- Contributed to 22%-24% of the crashes or near-crashes risks
(Klauer, Dingus, Neale, Sudweeks, & Ramsey, 2006)
- German Road Council (DVR) - one out of four fatal highway
crashes has been caused by drowsy drivers (Husar, 2010)
WACHsens project
- Partners
– Human Research Institut für Gesundheitstechnologie und Präventionsforschung GmbH – Institut für Fahrzeugtechnik, Technische Universität Graz – AVL United Kingdom
- Aim
– To develop a method in order to warn about impaired fitness to drive with the primary goal of drowsiness detection
- Objectives:
– to collect and merge data from different sensor as well as camera monitoring and driver behaviour observations gathered from simulator test drives – to identify changes in the attention, especially when driving automated, and to identify possible safety-relevant effects. – Observation: to estimated the tiredness level to combine it with the other data and to see if there are differences under different driving conditions
Study procedure
- 100 test persons gender and age balanced
- 2 states, well-rested and tired
– tired state: awake for 16 h continuously or the previous night with half of the sleeping time.
- 2 different driving modes in the simulator: manual and
automated (LK and CC) – manual-rested, automated-rested and manual-tired, automated-tired
- 30 minute monotonous drive on a highway during twilight
without traffic events
- avoid drinking alcohol or an unusual amount of caffeine-
content drinks
- Data collection
- vehicle-based measures:
– speed – steering wheel movement – position
- physiological signals:
– EEG and ECG – skin conductivity and respiration – eyelid and eye movements
- four cameras position
– front – face – 2x from the side
Observation procedure
- Two observers
- Using observation tool (excel based) for data
input and first calculations
- pausing video at each new activity of the