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
Drowsiness State Detection of Driver using Eyelid Movement- IRE Journal Confe...Vignesh C
A presentation on Drowsiness State Detection of Driver using Eyelid Movement in IRE Journal publications in Volume 2 Issue 10 2019. In the field of automobile, drowsiness causes more setbacks, which this presentation initiate a step in finding the solution.
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 project represents a way of developing an
interface to detect driver drowsiness based on continuously
monitoring eyes and DIP algorithms. Micro sleeps that are short
period of sleeps lasting 2 to 3 seconds are good indicator of
fatigue state. Thus by continuously monitoring the eyes of the
driver by using camera one can detect the sleepy state of driver
and timely warning is issued.
Aim of the project is to develop the hardware which is very
advanced product related to driver safety on the roads using
controller and image processing. This product detects driver
drowsiness and gives warning in form of alarm and as well as
decreases the speed of vehicle.Along with the drowsiness
detection process there is continuous monitoring of the distance
done by the Ultrasonic sensor. The ultrasonic sensor detects the
obstacle and accordingly warns the driver as well as decreases
speed of vehicle.
Tesla Autopilot is an advanced driver-assistance system feature offered by Tesla .That has lane centring, adaptive cruise control, self-parking, ability to automatically change lanes without requiring driver steering, and enables the car to be summoned to and from a garage or parking spot.
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.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
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
Drowsiness State Detection of Driver using Eyelid Movement- IRE Journal Confe...Vignesh C
A presentation on Drowsiness State Detection of Driver using Eyelid Movement in IRE Journal publications in Volume 2 Issue 10 2019. In the field of automobile, drowsiness causes more setbacks, which this presentation initiate a step in finding the solution.
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 project represents a way of developing an
interface to detect driver drowsiness based on continuously
monitoring eyes and DIP algorithms. Micro sleeps that are short
period of sleeps lasting 2 to 3 seconds are good indicator of
fatigue state. Thus by continuously monitoring the eyes of the
driver by using camera one can detect the sleepy state of driver
and timely warning is issued.
Aim of the project is to develop the hardware which is very
advanced product related to driver safety on the roads using
controller and image processing. This product detects driver
drowsiness and gives warning in form of alarm and as well as
decreases the speed of vehicle.Along with the drowsiness
detection process there is continuous monitoring of the distance
done by the Ultrasonic sensor. The ultrasonic sensor detects the
obstacle and accordingly warns the driver as well as decreases
speed of vehicle.
Tesla Autopilot is an advanced driver-assistance system feature offered by Tesla .That has lane centring, adaptive cruise control, self-parking, ability to automatically change lanes without requiring driver steering, and enables the car to be summoned to and from a garage or parking spot.
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.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
Driver Alertness On Android With Face And Eye Ball MovementsIJRES Journal
Drowsiness is a big problem while in driving specially in long and continues driving. This is a main cause for accidents. Maximum accidents found by the driver’s ignorance of seeing the road and focus on other thing that will divert the concentration. This project used to find sleepy drivers and lazy driver by monitoring them periodically. Main objective of the project to develop entire system in to smart phone and make it as user friendly to the driver and try to support the system on Smartphone have the Android Operating System. There are major things are considered for measure the fatigue level when monitoring driver, Eye movement driver. Smartphone camera capture the drives image, A Dynamic decision making used for find the drivers fatigue level. When driver reaches threshold level of fatigue, then alert is triggered to avoid accident and awake the driver. If driver ignores the alert and continue with drowsy driving, the alert system takes further steps to stop the vehicle. It may be find nearest coffee shop to refresh driver and also if he need other choices to refresh the map will help them.GPS and Navigation Service of the Android phones used for assist the driver to overcome his drowsy driver.
blue eyes technology is very useful technology and implementation of this technology make a revolution in field of engineering think when your P.C ask about your bad mood and perform some action that make you happy it is really very amazing and interesting.
AI Camera System to Prevent Road Accidents_1.pptxsanjivaniahire31
The "AI Camera System to Prevent Road Accidents" presentation introduces a cutting-edge solution leveraging artificial intelligence and computer vision technologies for enhanced road safety. This system utilizes advanced algorithms to analyze real-time video data from strategically placed cameras on roads. By employing deep learning techniques, the AI Camera System can detect and predict potential hazards, thus contributing to the prevention of road accidents. The presentation covers the system's features, benefits, and its role in intelligent transportation systems and smart city initiatives. It emphasizes the importance of proactive measures in traffic management and how this technology significantly improves overall road safety.
LIGHTWEIGHT HEADGEAR BRAIN WAVES FOR FATIGUE DETECTION IN SMARTPHONEijcsit
Fatigue is an annoying phenomenon that can affect people’s concentration on learning or working. It will
reduce the learning efficiency of people and even cause danger in working. For example, fatigue driving
results in car accident have been reporting from the daily news. In order to avoid the accidents mentioned
above, this research design the mobile system to detect fatigue to alert people by way of the lightweight
brainwave detector, and the smartphone that everyone owns nowadays. In this system, light weight head
mounted brainwave device is adopted and the signal are transmitted to the smartphone for further
processing. Our algorithm calculates the fatigue index by focus, eyes blink frequency, δ-wave, α-wave, βwave, and θ-wave captured from the brainwave to determine human’s spiritual condition. When the
experimental results were carried out to the car drivers, the system can remind drivers when they were
tired and drowsy while driving. This system can notify people to aware his own spiritual state so as to
raise working efficacy and avoid the occurrences of accident caused by fatigue. In addition, the whole
system without complex instrument and expensive cost.
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Technical seminar artificial passenger in ieee format
1. 1
Artificial Passenger
P.Manideep
B.tech 4th year Cse.
Kamala Institute of Technology
&Science Singapuram,Huzurabad
manideep.padakanti@gmail.com
B.Satish
Assoc.Professor, Dept. of CSE
Kamala Institute of Technology &
Science Singapuram,Huzurabad
saisujith2003@gmail.com
ABSTRACT
IBM (International Business Machines Corporation, NY) has
developed software that holds a conversation with the driver
to determine whether the driver can respond alertly enough,
called “Artificial Passenger”.This was designed to make long
solo journeys safer and more bearable. The Artificial
Passenger is an Artificial Intelligence based companion that
will be resident in software and chips embedded in the
automobile dashboard. The system has a conversation planner
that holds a profile of you, including details of your interests
and profession. A microphone picks up your answer and
breaks it down into separate words with speech-recognition
software. A camera built into the dashboard also tracks your
lip movements to improve the accuracy of speech recognition.
A voice analyzer then looks for signs of tiredness by checking
to see if the answer matches your profile. Slow responses and
lack of attention are signs of fatigue.
General Terms
Natural language e-companion.Sleep preventive device in cars
to overcomedrowsiness. Life safety system
Keywords
Condition Sensor, Mobile Indicator Device.
1.INTRODUCTION
The international business machines has developed a software
that holds a conversation with the driver to determine whether
the driver can respond alertly enough, called “Artificial
Passenger”This was designed in such a way that make long
solo journeys safer and more bearable.
2.BACKGROUND OF THE INVENTION
2.1Why Such System
According to a national survey in UK and USA, it is observed
that the driver fatigue annually is observed that the driver
fatigue annually cause:
• 10000 crashes
• 1500 deaths
• 7100 injuries
Majority of off-road accidents observed, were caused by eye
closure of half and even 2-3 caused by eye closure of half and
even 2-3 seconds, where the normal human eye blinks at
seconds, where the normal human eyeblinks at 0.2-0.3
seconds.
2.2 What is an Artificial Passenger?
Natural language e-companion. Sleep preventive device in
cars to overcome drowsiness. Life safety system.
2.3 What Does It Do
Detects alarm conditions through sensors. Broadcasts pre-
stored voice messages over the speakers. Captures images of
the driver.
2.4 About Artificial Passenger
The AP is an Artificial Intelligence based companion that will
be resident in software and chips embedded in the automobile
dashboard. The system has a conversation planner that holds
a profile of you, including details of your interests and
profession. A microphone picks up your answer and breaks it
down into separate words with speech-recognition software. A
camera built into the dashboard also tracks your lip
movements to improve the accuracy of speech recognition. A
camera built into the dashboard also tracks your lip
movements to improve the accuracy of speech recognition. A
voice analyzer then looks for signs of tiredness by checking to
see if the answer matches your profile. Slow responses and
lack of attention are signs of fatigue. If you reply quickly and
clearly, the system judges you to be alert and tells the
conversation planner to continue the line of questioning. If
your response is slow or doesn’t make sense, the voice
analyzer assumes you are dropping off and acts to get your
attention. If driver displays signs of fatigue, the artificial
passenger might be programmed to open all the windows,
sound a buzzer, increase background music volume, or even
spray the driver with ice water.
2.5 Devices Usedin Artificial Passenger
Eye tracker
Voice recognizer or speech recognizer
Natural language processor
Driver analyzer
Conversational planner
Alarm
Microphone
Camera
2.5.1 Working Components
Eye-Tracker
Collecting eye movement data requires both hardware and
software.
2. 2
Figure: 1
Hardware: Head-Mounted Systems or Remote Systems. Both
systems measure the corneal reflection of the infrared LED
which illuminates and generates a reflection off the surface of
the eye. This action causes the pupil to appear as a bright disk
in contrast to surrounding iris and creates a small glint
underneath the pupil which is used for calibration and
tracking.
Head mounted systems: Use multiple data points to record eye
movement and measures pupil glint from multiple angles.
Remote systems: In contrast, measure the orientation of the
eye relative to a fixed unit such as cameras.
Software: Data collection and analysis is handled by eye-
tracking software. Software catalogs eye-tracking in one of
the two ways. ERICA’s Eye Gaze software uses a small red x
to represent eye movement. In other, data is stored as a series
of x/y coordinates related to specific grid points on the
computer screen.
Automatic Speech Recognition
There are two ASRs used in the system:
First one is “Speaker Independent” and used for
decoding voice signals of the driver.Second one operates
with voice car media and decodes tapes, audio,
telephones, mails etc.
Natural Language Processor
Processes the decoded signals of voice or text data
provided from the ASR. Identifies related meanings from
the contents of the decoded messages. Produces variant
of responses. This output goes to the driver analyzer as
an input.
Driver Analyzer
Receives the textual data and voice data. Measures the
time response using a clock. Time responses, conclusions
about driver’s alertness will be passed on to the
conversational planner.
Conversational Planner
It is the heart of the system. Instructs thelanguage
generator to producethe response. If the driver is in
perfect condition CP instructs the language generator to
continue the conversation otherwise it will be instructed
to change the conversation.
The following is a typical scenario involving
Artificial Passenger. Imagine, driver “Mani”
returning home after late nights. His head starts to
nod.
AP: HeyMani, what did you get your daughter for her
birthday?
Mani: It’s not her birthday!
AP: You seem a little tired. Want to play a game?
Mani: Yes.
AP: You were a wiz at “Name that Tune” last time. I was
impressed. Want to try your hand at trivia?
Mani: ok.
AP: Pick a category: Hollywood Stars, Magic Moments
or Hall of Fame?
Mani: Hall of Fame.
Alarm
If the CP (conversational planner) receives information
that the driver is about to fall asleep an alarm system is
activated.
Microphone
For picking up words and separate them by some
internally used software for conversation.
Camera
This will track the lip movements of the driver. Used to
improve the accuracy of the speechrecognition.
Figure: 2
3. 3
1.6 Steps Involved in Tracking
Automatically initialize lips and eyes using color predicates
and connected components. Track lip corners using dark line
between lips and color predicate even through largemouth
movement like yawning. Construct a bounding box of the
head. Determine rotation using distances between eye and lip
feature points and sides of the face. Determine eye blinking
and eye closing using the number and intensity of pixels in the
eye region. Determine driver vigilance level using all acquired
information.
1.7 Detecting Driver Vigilance
Aiming a single camera at a head of the driver. Detecting
frequency of up and down nodding and left to right rotations
of the head within a selected time period with the camera.
Determining frequency of eye blinking and eye closing.
Determining frequency of yawning of the driver within the
selected time period with the camera. Generating an alarm
signal in real time if the frequency value of the up and down
nodding, the left to right rotations, the eye blinking, the eye
closings, the yawning exceeds a selected threshold value.
1.8 Applications
Interface withNeighboring Cars -Determines if a driver
presents a high safety risk for ex. falling asleep, tired,
inexperienced or under the influence of alcohol and signals
the cars nearby to be careful of the driver.
Medical Application -The system can monitor a driver and
detect if they are sick ,for ex. having a stroke or heart attack.
In any problem it alerts the vehicles nearby , so the driver
there can become alert. Opens and closes the doors and
windows of the car automatically. It is also used for the
entertainment. Also used in cabins in airplanes, trains, boats
etc.
1.9 Features of Artificial Passenger
IBM’s Artificial Passenger is like having a butler in your car-
someone who looks after you, takes care of your every need,
is bent on providing service, and has enough intelligence to
anticipate your needs. This voice-actuated telematics system
helps you perform certain actions within your car hands-free:
turn on the radio, switch stations, make a cell phone call, and
more. It provides uniform access to devices and networked
services in and outside your car. It reports car conditions and
external hazards with minimal distraction. Plus, it helps you
stay awake with some form of entertainment when it detects
you’re getting drowsy.
3.WORKLOAD MANAGEMENT
In this section we provide a brief analysis of the design of the
workload management that is a key component of driver
Safety Manager. An object of the workload manager is to
determine a moment-to-moment analysis of the user's
cognitive workload. It monitoring local and remote events,
andprioritizingmessagedelivery.There is rapid growth in the u
se of sensory technology in cars. These sensors allow for the
monitoring of driver actions (e.g. application of brakes,
changing lanes), provide information about local events (e.g.
heavy rain), and provide information about driver
characteristics (e.g. speaking speed, eyelid status)There is also
growing amount of distracting information that may be
presented to the driver (e.g. phone rings, radio, music, e-mail
etc.) and actions that a driver can perform in cars via voice
control. The relationship between a driver and a car should be
consistent with the information from sensors. The workload
manager should be designed in such a way that it can integrate
sensor information and rules on when and if distracting
information is delivered. This can be designed as a “workload
representational surface”. One axis of the surface would
represent stress on the vehicles and another, orthogonally
distinct axis, would represent stress on the driver. Values on
each axis could conceivably run from zero to one. Maximum
load would be represented by the position where there is both
maximum vehicle stress and maximum driver stress, beyond
which there would be “overload”. Workload manager is
closely related to the event manager that detects when to
trigger actions and/or make decisions about potential actions.
The system uses a set of rules for starting and stopping the
interactions (or interventions).It controls interruption of a
dialog between the driver and the car dashboard (for example,
interrupting a conversation to deliver an urgent message about
traffic conditions on unexpected driver route).It can use
answers from the driver and/or data from the workload
manager relating to driver conditions, like computing how
often the driver answered correctly and the length of delays in
answers, etc. It interprets the status of a driver’s alertness,
based on his/her answers as well as information from the
workload manager. It will make decisions on whether the
driver needs additional stimuli and on what types of stimuli
should be provided (e.g. verbal stimuli via speech applications
or physical stimuli such as a bright light, loud noise, etc.)And
whether to suggest to a driver to stop for rest. The system
permits the use and testing of different statistical models for
interpreting driver answers and information about driver
conditions. The driver workload manager is connected to a
driving risk evaluator that is an important component of the
Safety
4.Privacy Aspects
Addressingprivacy concerns:Thesafety driver manager
framework should be designed such that it will be
straightforward for the application designers to protect theend
user’s privacy. This should include encryption of themessage
traffic from thevehicle, through carrier’s network, and into
the service provider’s secure environment, such that the
driver’s responses cannot be intercepted.This can be achieved
through the use of IBM Web Sphere Personalization Server or
Portal Server, allowing the end user an interface to select
options and choices about the level of privacy and/or the types
of content presented. An example of such an option is the
opportunity for drivers to be informed about theexistence
of the Artificial Passenger capability, with clear instructions
about how to turn it off if they opt not to use it.
5. 5
6.FUTURE IMPLEMENTATION
Will provide us with shortest time routing based on road
conditions changing because of weather and traffic,
information about the cars on the route, destination
requirement.
7.CONCLUSION
Method for monitoring driver alertness sufficient time to avert
an accident. Successful implementation of Artificial
Passenger would allow use of various services in car like
reading emails, navigation, downloading music files, voice
games etc. compromising on driver safety. We suggested that
such important issues related to a driver safety, such as
controlling telematics devices and drowsiness can be
addressed by a special speech interface. This interface
requires interactions with workload, dialog, event, privacy,
situation and other modules. We showed that basic speech
interactions can be done in a low-resource embedded
processor and this allows a development of a useful local
component of Safety Driver Manager. The reduction of
conventional speech processes to low – resources processing
was done by reducing a signal processing and decoding load
in such a way that it did not significantly affect decoding
accuracy and by the development of quasi-NLU principles.
8.ACKNOWLEDGMENTS
I would like to Thank my guide respected B.Satish sir Assoc.
Professor, Dept. of CSE who had supported meand guided
me for doing this paper.
9.REFERENCES
[1].http://www.Google.com
[2].http://www.Wikipedia.org/wiki/Artificial_Passenger
[3].https://www.scribd.com/document/Artificial-Passenger
[4].http://www.springerlink.com/content/passenger