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

3. Driver distraction Driver distraction and inattention has become a leading cause of motor-vehicle crashes o 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 IVISs o Benefits of the IVIS functions o Safety o One solution: driver distraction mitigation systems People use In-Vehicle Information Systems (IVISs) during driving 2

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

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

6. 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 precisely o Data mining techniques can detect non-linear and time-dependent relationships 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) 5

7. 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 movements Driving performance Eye movement pattern

8. 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

9. 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

10. 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

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

12. 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 2 5 –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) 11

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

14. 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 13

15. 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 data o Temporal eye movement measures o Spatial eye movement measures o Driving performance measures The higher layer: DBNs identify cognitive state from the feature behavior (cluster labels) with consideration of time dependency 14 Different from clustering, supervised clustering more likely produce meaningful clusters in terms of driver cognitive state.

16. Supervised clustering categorize classified data 15 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.

17. 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) |curr| THEN curr:=s ELSE terminate and return curr as the solution for this run Report the best out of the r solutions found 16

18. Thank you !! Questions ?? 17