The Detection of Driver Cognitive Distraction Using Data Mining Methods
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Transcript of The Detection of Driver Cognitive Distraction Using Data Mining Methods
The Detection of Driver Cognitive Distraction Using Data Mining Methods
Presenter: Yulan LiangDepartment of Mechanical and Industrial EngineeringThe University of Iowa
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Driver distraction
• Driver distraction and inattention has become a leading cause of motor-vehicle crasheso Nearly 80% of crashes and 65% of
near-crashes (the 100-car study)o Increasing use of In-Vehicle
Information Systems (IVISs), such as, navigation systems, MP3 players, and internet services.
• Driver distraction represent a big challenge for developing IVISso Benefits of the IVIS functionso Safety o One solution: driver distraction
mitigation systems People use In-Vehicle Information Systems (IVISs) during driving
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Driver distraction mitigation systems
• Distraction detection is a crucial functiono Cognitive distractiono Visual/manual distractiono Simultaneous(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
...
Indicators of distractionDetection techniques
An overview of driver distraction mitigation systems 3
Indicators of driver distraction
• Cognitive distraction (subtle, no direct measures of “mind off road”)
o Concentrate gaze distributiono Impair information consolidationo Degrade driving performance (less serious and consistent)o Impair driver adaptation in tactical driving
Performance indicators:
--Driving performance (less serious and consistent)Abrupt steering controlLarge lane-position variability
Miss safety-critical events
--Eye gazeDuration and location of fixationsDistance of saccadesDuration, location, distance, and speed of smooth pursuits
Suitable for real-time detection
Not suitable for real-time detection
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Detection algorithm for driver distraction
• Driving is complex and continuous human behavior
• Data mining approaches are suitable to detect driver distraction o Insufficient knowledge impedes using theories to detect distraction preciselyo Data mining techniques can detect non-linear and time-dependent relationshipso 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)
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Bayesian Networks (BNs)
• To model probabilistic relationship among variables– wide applications, especially
modeling human behavior
• Three kinds of variables– Hypothesis, evidence, hidden
• Conditional dependency
Bayesian Networks (BNs)
Cognitive distraction
Eye movementsDriving performance
Eye movement pattern
Static and Dynamic BNs
• Static BNs (SBNs) – in single time point
• Dynamic BNs (DBNs)– across time (Markov process)
• Comparison btw SVM and BNs– Both can model complex relationships– Results of BNs can quantify relationships using
information theory measures (such as mutual information)– DBNs can model time-dependent relationship– SVMs are more computational efficient than BNs.
A dynamic BN
Methods
• Data source – two cognitive conditions
• auditory stock ticker: tracking the change and overall trends of two stock prices» without visual distractors
• 4 IVIS drives and 2 baseline drives (15 minutes each)• to define distraction for models
– data collection (60Hz)• eye movements
» gaze screen intersection coordinates
• Driving performance» lane and steering position
Driving scenario
Data reduction
• Eye movements– eye data eye movements– 7 eye movement measures
• 3 driving performance measures– lane position – steer wheel position– steering error
Plot of eye data
fixation
smooth pursuit
blink frequency
-duration-position
-duration-distance-speed-direction
Training Data
measures
…...
…...
Summarizationacross window
summarizedinstances
…...
trainingdata
SBNs, SVMs
…...
randomselection
• Summarization– window size(5, 10, 15, or 30 s)
• Training data– SBNs SVMs– DBNs– 2/3 of total data
DBNs
…...
(19 measures)
SVM and BN training parameters
• SVMs– Radial Basis Function (RBF) – 10-fold-cross-validation to obtain C and γ in the range of 2-5 to 25
– Continuous predictors (performance measures)
– “LIBSVM” Matlab toolbox
• BNs– No hidden node and constrained network structure– Training sequences for DBN –120 seconds long– Discrete predictors– a Matlab toolbox
(Murphy) and an accompanying structural learning package (LeRay)
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2
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ji exxK
Using SVMs and DBNs to detect cognitive distraction
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SVM prediction for a participant
Comparison between BNs and SVMs
d'
)()(' 11 FAHITd
• Changes in drivers’ eye movements and driving performance over time are important predictors of cognitive distraction.
• SVMs have some advantages over SBNs– Parameter selection: 10-fold across-validation– Computational ease: training time
• Improving algorithm– Consider time-dependent relationship in behavior– Reduce computational load
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A layered algorithm to detect cognitive distraction
• Off-line supervised clustering identifies multiple feature behavior based on subset of behavioral measures based on the training datao Temporal eye movement measureso Spatial eye movement measureso Driving performance measures
• The higher layer: DBNs identify cognitive state from the feature behavior(cluster labels) with consideration of time dependency
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Different from clustering, supervised clustering more likely produce meaningful clusters in terms of driver cognitive state.
Supervised clustering
• categorize classified data
15A. Traditional clustering B. Supervised clustering
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1' 1'’
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The fitness function of supervised clustering (Zeidat et al., 2006) X is a clustering solution, β is the parameter to balance the ratio of impurity and penalty in the fitness function, k is the number of clusters in X, n is the total number of data, and c is the number of classes in the data.
Supervised clustering algorithm
• Single Representative Insertion/Deletion Steepest Decent Hill Climbing with Randomized Restart – repeat something similar to SPAM r times and chose the best
• REPEAT r TIMES– curr = a randomly created set of representatives (with size between c+1 and
c)– WHILE not done DO
• Create new solution S by adding a non-representative or removing a representative in curr (if size(curr) = k’, new possible solutions are in size of k’+1 and k’-1 )
• Determine the element s and S for which the objective function in SPAM q(s) is minimal (if there is more than one minimal element, randomly pick one)
• IF q(s)<q(curr) THEN curr:=sELSE IF q(s)=q(curr) AND |s|>|curr| THEN curr:=sELSE terminate and return curr as the solution for this run
• Report the best out of the r solutions found
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Thank you !!Questions ??
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