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There is accumulating evidence that driver distrac- tion is a leading cause of vehicle crashes and incidents. In par- ticular, increased use of so-called in-vehicle information systems (IVIS) have raised important and growing safety concerns. Thus, detecting the driver’s state is of paramount importance, to adapt IVIS, therefore avoiding or mitigating their possible negative effects. The purpose of this presentation is to show a method for the nonintrusive and real- time detection of visual distraction, using vehicle dynamics data and without using the eye-tracker data as inputs to classifiers. Specifically, we present and compare different models that are based on well-known machine learning (ML) methods. Data for training the models were collected using a static driving simulator, with real human subjects performing a specific secondary task [i.e., a surrogate visual research task (SURT)] while driving. Different training methods, model characteristics, and feature selection criteria have been compared. Based on our results, using a BSN has outperformed all the other ML methods, providing the highest classification rate for most of the subjects. Index Terms—Accident prevention, artificial intelligence and machine learning (ML), driver distraction and inattention, intel- ligent supporting systems.
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Real-Time Detection System of Driver Distraction
Using Machine Learning
Contents…
Machine Learning Introduction Driver Distraction Driver Distraction Mitigation Detection Algorithm Methods Advantages / Disadvantages Applications
MACHINE LEARNINGMachine learning is a scientific
discipline concerned with the design and development of algorithms that allow machines to mimic human intelligence.
Introduction
80% of crashes and 65% of near crashes involved some sort of driver distraction.
Teens are 4x more likely to be in a wreck than drivers over age 30.
Motor vehicle crashes are the leading cause of death for 16-20 year olds.
Driver Distraction
• Driver distraction and
inattention has become a leading
cause of motor-vehicle crashes
IVIS
• Driver distraction represent a
big challenge for developing
IVISs
Types of Distractions:
There are 3 types of distractions:
Visual Distractions: Anything that takes your eyes off the road.
Manual Distractions: Anything that takes your hands off the steering wheel.
Cognitive Distractions: Anything that takes your mind off driving.
Distractions: All distractions can be dangerous and life
threatening but texting is one of the most dangerous distractions because it involves all three types of distractions.
Other distractive activities include:» Using a cell phone» Eating and drinking» Talking to passengers» Grooming» Reading, including map» Using PDA or navigation system» Watching a video» Changing the radio station, CD, Mp3 player or other device
How Cell Phones Distract
Visual – Eyes off roadMechanical – Hands off wheelCognitive – Mind off driving
CHALLENGE: Drivers don’t
understand or realize that talking
on a cell phone distracts the brain
and takes focus away from the
primary task of driving.
Sleepy Driving
Sleepy Driving…
100,000 reported crashes per year are as a result
of drowsiness. 1,500 of them result in deaths.
55% of those crashes were caused by drivers
under the age of 25.
Driver distraction mitigation systems
Distraction detection is a crucial function
oCognitive distractionoVisual/manual distractionoSimultaneous(dual) distraction
Driver state-----------------· Physiological responses
· eye glances· fixations, saccades, and
smooth pursuits ...
Driver input-----------------· Steer
· Throttle· Brake
...
Vehicle state---------------· Lane position· Acceleration
· Speed ...
Visual/Manual distraction
Cognitive distraction
Model-based Driver Distraction Detection
Mitigation strategy
Focus of dissertation
SensorTechology
MitigationSystem
Strategy n
Strategy 2
Strategy 1
...
An overview of driver distraction mitigation systems
Detection algorithm for driver distraction
• Driving is complex and continuous human behavior
• Machine learning approaches are suitable to detect driver distraction o Linear regression, decision tree, Support Vector Machines
(SVMs), and Bayesian Networks (BNs) have been used to identify various distractions
Support Vector Machines (SVMs)
Bayesian Networks (BNs)
Bayesian Networks (BNs)
To model probabilistic relationship among variables◦ wide applications, especially
modeling human behavior
Three kinds of variables◦ Hypothesis, evidence, hidden
H
E3E2E1
S
Bayesian Networks (BNs)
Cognitive distraction
Eye movementsDriving performance
Eye movement pattern
Methods
SURT(surrogate visual research task) display on the right part of
the driving simulator cockpit
Advantages…Intelligent Decisions Self modifying Multiple iterations
Method implementationUse of different methodsTest in diverse conditions.
Disdvantages…
ApplicationsComputer vision : design and implementation
of algorithms that can automatically process visual data.
Information retrieval : Technologies in order to help solve complex and challenging business problems.
Robot locomotion : Capabilities for robots to decide how, when, and where to move
REMEMBER…
The life you save could be your own!
Conclusion..The most ambitious goals of automatic
learning systems is to mimic the learning capability of humans, and the capability of humans to drive is widely based on experience, particularly on the possibility of learning from experience. ML approaches can outperform the traditional analytical methods. Moreover, a human’s mental and physical driving behavior is non deterministic.
QUESTIONS...
THANKYOU
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