The Detection of Driver Cognitive Distraction Using Data Mining Methods

1. The Detection of Driver Cognitive Distraction Using Data Mining Methods Presenter: Yulan Liang Department of Mechanical and Industrial Engineering The University of Iowa

2. Driver distraction • Driver distraction and inattention has become a leading cause of motor-vehicle crashes • Nearly 80% of crashes and 65% of near-crashes (the 100-car study) • 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 IVISs • Benefits of the IVIS functions • Safety • One solution: driver distraction mitigation systems People use In-Vehicle Information Systems (IVISs) during driving

3. Driver distraction mitigation systems • Indicators of distraction • Detection techniques • Distraction detection is a crucial function • Cognitive distraction • Visual/manual distraction • Simultaneous(dual) distraction An overview of driver distraction mitigation systems

4. Indicators of driver distraction • Cognitive distraction (subtle, no direct measures of “mind off road”) • Concentrate gaze distribution • Impair information consolidation • Degrade driving performance (less serious and consistent) • Impair driver adaptation in tactical driving Suitable for real-time detection Performance indicators: --Eye gaze Duration and location of fixations Distance of saccades Duration, location, distance, and speed of smooth pursuits --Driving performance (less serious and consistent) Abrupt steering control Large lane-position variability Miss safety-critical events Not suitable for real-time detection

5. Detection algorithm for driver distraction • Driving is complex and continuous human behavior • Data mining approaches are suitable to detect driver distraction • Insufficient knowledge impedes using theories to detect distraction precisely • Data mining techniques can detect non-linear and time-dependent relationships • 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)

6. Bayesian Networks (BNs) Cognitive distraction • To model probabilistic relationship among variables • wide applications, especially modeling human behavior • Three kinds of variables • Hypothesis, evidence, hidden • Conditional dependency Eye movement pattern Bayesian Networks (BNs) Eye movementsDriving performance

7. 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

8. 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

9. Data reduction Plot of eye data • Eye movements • eye data eye movements • 7 eye movement measures • 3 driving performance measures • lane position • steer wheel position • steering error fixation -duration-position smooth pursuit -duration-distance -speed -direction blink frequency

10. Training Data • Summarization • window size • (5, 10, 15, or 30 s) • Training data • SBNs SVMs • DBNs • 2/3 of total data (19 measures)

11. 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)

12. Using SVMs and DBNs to detect cognitive distraction SVM prediction for a participant d' Comparison between BNs and SVMs

13. 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

14. A layered algorithm to detect cognitive distraction Different from clustering, supervised clustering more likely produce meaningful clusters in terms of driver cognitive state. • Off-line supervised clustering identifies multiple feature behavior based on subset of behavioral measures based on the training data • Temporal eye movement measures • Spatial eye movement measures • Driving performance measures • The higher layer: DBNsidentify cognitive state from the feature behavior(cluster labels) with consideration of time dependency

15. Supervised clustering 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. • categorize classified data

16. 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

17. Thank you !! Questions ??