Automated Driver Fatigue Detection and Road Accident Prevention System: An Intelligent Approach to Solve a Fatal Problem. At least 4,284 people, including 516 women and 539 children, were killed and 9,112 others were injured in 3,472 road accidents across Bangladesh in 2017. Some of those accidents could have been avoided if proper systems were implemented at the time. This project focuses on creating a system based on EEG (Electroencephalogram) and ECG (electrocardiogram) signal from driver which will alert a driver about drowsiness while driving.
This Presentation is on the topic of Driver drowsiness Detection .
In this presentation We will discuss the Techniques used to detect drowsiness and compare some techniques
In the end we conclude and provide some suggestions regarding future work.
Thanks
it is a presentation based on image processing used in the field of fatigue detection while driving which can save many life as well as prevent accident.
Automated Driver Fatigue Detection and Road Accident Prevention System: An Intelligent Approach to Solve a Fatal Problem. At least 4,284 people, including 516 women and 539 children, were killed and 9,112 others were injured in 3,472 road accidents across Bangladesh in 2017. Some of those accidents could have been avoided if proper systems were implemented at the time. This project focuses on creating a system based on EEG (Electroencephalogram) and ECG (electrocardiogram) signal from driver which will alert a driver about drowsiness while driving.
This Presentation is on the topic of Driver drowsiness Detection .
In this presentation We will discuss the Techniques used to detect drowsiness and compare some techniques
In the end we conclude and provide some suggestions regarding future work.
Thanks
it is a presentation based on image processing used in the field of fatigue detection while driving which can save many life as well as prevent accident.
Presentation of motorbike use cases of EU funded project ADAS&ME. The goals of these use cases are
• Integrate monitoring sensors in the protective gear without affecting comfort
• Develop algorithms able to detect physical fatigue, distraction and, optionally, stress
• Create a HMI strategy that assists the rider during long tours and effectively incites the rider to stop when his/her state gets critical.
• Develop safe active strategies to be used when the situation gets safety critical.
You will find further information at: www.adasandme.com and twitter @adasme.
Presentation of motorbike use cases of EU funded project ADAS&ME. The goals of these use cases are
• Integrate monitoring sensors in the protective gear without affecting comfort
• Develop algorithms able to detect physical fatigue, distraction and, optionally, stress
• Create a HMI strategy that assists the rider during long tours and effectively incites the rider to stop when his/her state gets critical.
• Develop safe active strategies to be used when the situation gets safety critical.
You will find further information at: www.adasandme.com and twitter @adasme.
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1. The Differences of Driving Skills of Experienced and
Inexperienced Drivers in Unexpected Driving Situations
V.-M. Nurkkala, K. Koskela, J. Kalermo, and T. Järvilehto
Sixth International Conference on Driver Behaviour and Training
Helsinki 19.-20.8.2013
2. CSE Driving Simulation Center
• CEMIS Driving Simulation Center works in co-operation with Kajaani
University of Applied Sciences, University of Oulu and CEMIS –
Center for Measurement and Information Systems in Finland.
• Research has focused on exploring how driving skills develop,
particularly from the point of view of learning and anticipation.
3. CSE Driving Simulation Center
CSE Center has multi-disciplinary expertise in driving simulation, virtual
environments, usability, behavioral research as well as information
systems and engineering. The staff has expertise in experimental
psychology, physiological measurements and eye tracking.
4. Driver behaviour and driving simulators
• CreaVehicle project at the University of Oulu and
Kajaani University of Applied Sciences in 2010-2011
• Project goal was to develop a driving simulation
environment for research and training purposes
• A goal was also to develop research methodology
and start driver’s behaviour research
• Project was funded by European Union Regional
Fund, Joint Authority of Kainuu and Sunit Oy
5. CSE II Driving Simulator
2012-2013
The modified Volkswagen Polo
driving simulator
- Simulator software: SCANeR
Studio, Oktal
- Motion platform: 6DOF, MeVEA
CSE I Driving Simulator
2010-2011
A low-cost driving simulator for
research, education, testing and
development. Also a learning
environment for students and
teachers.
6. Virtual Kajaani
Virtual model of our hometown
– Objects created in 3DS Max and Google SketchUp
– Terrain created based on Kajaani shapefiles
– Both imported to SCANeR Studio
– Road surface, road logic etc. created in SCANeR
In collaboration with Ataverti
8. Studies in CSE Driving Simulation Center
• Simulator sickness research
– A new method to study the temporal appearance of simulator sickness
(Temporal Measurement of Simulator Sickness, TMMS).
– TMSS helps to develop the simulator environment further and decrease
simulator sickness rate.
• We were keen to know could anticipation and very short response times
be seen e.g. in unexpected driving situations.
– Encountering an unexpected situation often leaves very little time for the driver to
act in order to avoid an accident
– Anticipatory driving is considered essential in successful and quick acting in sudden
occurrences
– In background are our previous studies of fast skilled actions related to
reading and ball games
9. Research on driving behaviour
• The research goal was to explore
– What is the relation between learning and driving behaviour in terms
of development of anticipation?
– How do experienced driver’s perform in unexpected driving situations
in comparison to novice drivers?
• The experiment consisted of two parts
– In the first one, the subjects (Ss; N=22) had to learn the shortest route
through a virtual city.
– In the second one, four unexpected situations were added in the
learned route, and the Ss drove twice the same route.
10. Related work
• Brake reaction times has been a topic for numerous studies
(e.g., Olson & Sivak, 1986; Green, 2000; Summala, 2000)
• Alertness affects reaction time
– Green (2000) divides driver alertness into three classes:
expected, unexpected and surprise
• Green (2000) provides several estimates for reaction times:
– Expected: 0.7 s.
– Unexpected: about 1.25 s.
– Surprise: 1.5 s.
11. Related work
• Also criticality/urgency of situation affects reaction time
• Summala (2000) proposes some faster brake reaction times,
referring to criticality of the situation
– Unexpected that are critical: below 1.0 s.
– Surprise that are critical: 1.0-1.3 s.
12. Polar S810i
NeurOne EEG/ERP device
EyeLink II eye tracking system
• Motion platform (3DOF) and cushion shaker
• Frex steering wheel, pedals and gear shifter
• Volvo seat, middle console and hand break
• Blinkers
• Gauge screen
• High quality 5.1 speakers
• Powerful computer with three nVidia graphic cards
• Curved screen (view angle 220°)
• Telemetry recording system
Research methods
13. Research methods
• Subjects Ss; N=22
– 16 Finnish policemen, age 23-56
• “Experienced” 23-39 years
• “Very experienced” 40-56 years
– 6 “novice” drivers, age 21-34
• Driver’s licence < 4 years & driving < 4000 km/year
• Driving behaviour was examined by measuring EMG (tibialis
anterior, gastrocnemius, trapezius and deltoideus), heart rate
variability, eye movements, the amount of driving errors and
changes of driving controls
• The present analysis includes data from 8 policemen and 4
novice drivers, from four unexpected situations
15. Results – outline
• In the analysis we’ll concentrate on the
following issues:
– What kind of action strategies did the subjects
have in unexpected driving situations?
– Was there a difference between response times of
experienced drivers and novice drivers in different
situations?
– Where in the measured parameters were the
biggest differences between the experienced and
novice drivers?
16. Situation1 (surprise)
Results – action strategies to avoid the crash
Situation1
Surprise, critical
Braking onlyLoosening the throttle
and steering
Loosening the throttle
and braking
Loosening the throttle,
braking and steering
Novice
Experienced
Very experienced
18. Throttle-brake interval
Time interval between to start loosening the throttle to start braking
Throttle-brakeinterval(ms)
Novice
Experienced
Very experienced
Situation
19. Conclusions
• The results show that the response times of the
experienced drivers are very shorter in unexpected
driving situations than those of novice drivers.
• In the first situation (bridge), very short throttle-
brake interval was measured from very experienced
drivers.
• Even if there were no remarkable differences in
response times, very experienced drivers started
braking earlier (throttle-brake interval).