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The Detection of Driver Cognitive Distraction Using Data Mining Methods Presenter: Yulan Liang Department of Mechanical and Industrial Engineering The University of Iowa 1 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 Driver distraction mitigation systems • Distraction detection is a crucial function o Cognitive distraction o Visual/manual distraction o Simultaneous(dual) distraction Sensor Techology Model-based Driver Distraction Detection Indicators of distraction Detection techniques Mitigation System Driver state----------------· Physiological responses · eye glances · fixations, saccades, and smooth pursuits ... Visual/Manual distraction Strategy n Driver input----------------· Steer · Throttle · Brake ... Vehicle state--------------· Lane position · Acceleration · Speed ... Strategy 2 Cognitive distraction . . . Mitigation strategy Strategy 1 Focus of dissertation An overview of driver distraction mitigation systems 3 Indicators of driver distraction • Cognitive distraction (subtle, no direct measures of “mind off road”) o o o o Concentrate gaze distribution Impair information consolidation Degrade driving performance (less serious and consistent) Impair driver adaptation in tactical driving Performance indicators: Suitable for realtime detection --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 realtime detection 4 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 Bayesian Networks (BNs) • To model probabilistic relationship among variables H – wide applications, especially modeling human behavior • Three kinds of variables – Hypothesis, evidence, hidden • Conditional dependency Cognitive distraction Eye movement pattern S E1 E2 E3 Bayesian Networks (BNs) Eye movements Driving performance Static and Dynamic BNs • Static BNs (SBNs) – in single time point • Dynamic BNs (DBNs) – across time (Markov process) t-2 Ht-1 Ht t-2 St-1 St E1t-1 E2t-1 E3t-1 T=t-1 E1t E2t E3t T=t A dynamic BN • 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. 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 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 Training Data measures …... summarized instances (19 measures) training data • Summarization SBNs, SVMs – window size (5, 10, 15, or 30 s) …... • Training data DBNs …... …... …... Summarization across window random selection – SBNs SVMs – DBNs – 2/3 of total data SVM and BN training parameters • SVMs – – – – xi x j 2 Radial Basis Function (RBF) K xi , x j e 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) H1 E1 1 E2 1 T=1 H2 E3 1 E1 2 E2 2 T=2 E3 É ... Ht É É ... ... É ... 2 E1 É ... t E2 t E3 T=t 11 t Using SVMs and DBNs to detect cognitive distraction SVM prediction for a participant d' Comparison between BNs and SVMs d ' 1 ( HIT ) 1 ( FA) 12 • 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 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 Different from clustering, supervised clustering more likely produce meaningful clusters in terms of driver cognitive state. • The higher layer: DBNs identify cognitive state from the feature behavior (cluster labels) with consideration of time dependency 14 Supervised clustering • categorize classified data 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. 1' 1'’ 1 3 2 3' 2 4 A. Traditional clustering B. Supervised clustering 15 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:=s ELSE IF q(s)=q(curr) AND |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 Thank you !! Questions ?? 17