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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Using SVMs and DBNs to detect cognitive distraction

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SVM prediction for a participant

Comparison between BNs and SVMs

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• 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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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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