Center for Intelligent Systems Research GW Transportation Research Institute

1. NDIA 3rd Annual Intelligent Vehicle Systems Symposium Driving Simulator Experiment:Detecting Driver Fatigue by Monitoring Eye and Steering Activity Center for Intelligent Systems Research GW Transportation Research Institute The George Washington University, Virginia Campus, 20101 Academic Way, Ashburn, VA 20147 Dr. Azim Eskandarian, Riaz Sayed (GWU)

2. Research Objective Conduct Simulator Experiment and Analyze the Data, to search for a system for automatic detection of drowsiness based on driver’s performance

3. Significance of the Problem • Drowsiness/Fatigue Related Accident Data: • NHTSA Estimates 100,000 drowsiness/fatigue related Crashes Annually • FARS indicates an annual average of 1,544 fatalities • Fatigue has been estimated to be involved in 10-40% of crashes on highways (rural Interstate) • 15% of single vehicle fatal truck crashes • Fatigue is the most frequent contributor to crashes in which a truck driver was fatally injured

4. Significance of the Problem • A drowsy/sleepy driver is unable to determine when he/she will have an uncontrolled sleep onset • Fall asleep crashes are very serious in terms of injury severity • An accident involving driver drowsiness has a high fatality rate because the perception, recognition, and vehicle control abilities reduces sharply while falling asleep • Driver drowsiness detection technologies can reduce the risk of a catastrophic accident by warning the driver of his/her drowsiness

5. Driver Drowsiness Detection Techniques 1. Sensing of driver physical and physiological phenomenon • Analyzing changes in brain wave or EEG • Analyzing changes in eye activity and Facial expressions • Good detection accuracy is achieved by these techniques • Disadvantages: • Electrodes have to be attached to the body of the driver for sensing the signals • Non-contact type sensing is also highly dependant on environmental conditions

6. Driver Drowsiness Detection Techniques 2. Analyzing changes in performance output of the vehicle hardware • Steering, speed, acceleration, lateral position, and braking etc. • Advantages: • No wires, cameras, monitors or other devices are to be attached or aimed at the driver • Due to the non-obtrusive nature of these methods they are more practically applicable

7. Approach for Drowsiness Detection and Driver Warning

8. Experiment • Conducted in the Vehicle Simulator Lab of the CISR. GWU VA Campus, Ashburn VA. • Twelve subjects between the ages of 23 and 43 • Test Scenario consisted of a continuous rural Interstate highway, with traffic in both directions Speed limit of 55 mph. • Morning session 8 – 10 am • Night session 1 – 3 am

9. CISR Driving Simulator

10. Eye Tracking Equipment

11. Sample Data From Simulator RUN#ZONETIMESPEEDLIMCRASHBCRASHVLANEXBRAKEFORBRAKETAP 1 0 35 0 0 0 0 0 1 2.1 35 0 0 0 0 0 1 4.2 35 0 0 0 0 0 1 6.2 35 0 0 0 0 0 1 8.3 35 0 0 0 0 0 STEERPOSSTEERVARLATPLACE LATPLVARSPEEDSPEEDVARSPEEDDEV -0.1 0 -0.09 0 53.71 0 -4.65 0.2 0 -0.22 0 53.71 0 -4.65 0.4 0 -0.31 0 53.71 0 -4.65 0 0 -0.35 0 53.71 0 -4.65

12. Lateral Position of Vehicle

13. Power Spectrum Density for Vehicle Lateral Position

14. Steering Anglefilter correction for curves

15. Hypothesis • The hypothesized relationship between driver state of alertness and steering wheel position is that under an alert state, drivers make small amplitude movements of the steering wheel, corresponding to small adjustments in vehicle trajectory, but under a drowsy state, these movements become less precise and larger in amplitude resulting in sharp changes in trajectory (Planque et al. 1991).

16. Unsupervised Layer : Clustering Competitive Algorithm Supervised Layer: Classification Feedforward Algorithm A Hybrid Artificial Neural Network Architecture Wj1 2 8 X 8

17. Hybrid Artificial Neural Network Architecture

18. ANN Training for Unsupervised Competitive Layer 1. Initialize the weight vector randomly for each neuron. 2. Present the input vector X(n) . 3. Compute the winning neuron using the Euclidean distance as a metric. Where Wi= [w1, w2, …. w8]T is the weight vector of neuron i. bi is the bias to stop the formation of dead neurons.

19. ANN Training Competitive Layer Continued • N number of time a neuron wins in competitive layer •  and are learning constants and o(n) is the outcome of the present competition (=1 if neuron wins & else = 0). • Ci initially set to small random value • 4. Update the weight vector of the winning neuron Wi* only. • 5. Continue with step (2) two until change in the weight vectors reaches a minimum value.

20. ANN Training Competitive Layer Continued • The competitive algorithm moves the weight vectors of all the neurons closer to the center of the clusters. • Each neuron (or set of neurons) of the competitive layer represents a cluster. • The Output of the neuron is 1 if it wins the competition and 0 if it losses. • The Output of the Competitive layer is an n-dimensional binary vector T(n) = [t1, t2, …….., tn]T .

21. ANN Training for supervised feed forward layer • Step 1: Initialize the synaptic weights and the thresholds to small random numbers. • Step 2: Present the network with an epoch of training exemplars • Step 3: Apply Input vector X(n) to the input layer and the desired response d(n) to the output layer of neurons. The output of each neuron is calculated as

22. ANN Training Continued

23. ANN Training Continued • N = No. of training sets in one epoch •  = Learning rate parameter •  = Momentum constant • Step 5: Iterate the computation by presenting new epochs of training examples until the mean square error (MSE) computed over entire epoch achieve a minimum value. MSE is given by:

24. ANN Training Parameters • Hybrid architecture using an unsupervised clustering algorithm and a classifier (Back propagation learning algorithm in batch mode) • Tanhyperbolic activation function, with output range from –1 to 1 • Variable learning rate and momentum were used • Cross validation during training

25. Input Discretization of Steering Angle Algorithm to select r (ranges) for each driver to compensate performance variability between drivers Discretized steering angle for one driver :

26. Accounting for Individual Driver Behaviors • Some drivers are more “sensitive” to vehicle lateral position and make very accurate corrections to the steering for lane keeping while other are less “sensitive” and make less accurate corrections. • The result is a low amplitude signal (steering angle) for more “sensitive” drivers and relatively high amplitude signal for less “sensitive” drivers. • Larger values for Pk will make the descritization ranges wider to accommodate large amplitude while small values will make them shorter for small amplitudes. • Therefore, same ANN (8-dimensional descritization) can be used

27. Input Discretization of Eye closures • Eye closure data is recorded at 60 Hz • Ci = No. of zero’s in 1 second of data • Ci is further discretized according to the following scheme

28. Input Discretization of Eye closures Algorithm to select r (ranges) for each driver to compensate eye closure variability between drivers P values are representative of variability of eye closures (blinking) for each driver Sample of a few seconds of Discretized Eye closures for one driver :

29. Input Vector • The two vectors are combined to form a 12 dim vector J(T) • Vector J(T) is summed over 15 sec time interval to get the input vector X(n)

30. Input and Desired Output Vector Each row represents the sum of discretized input over a selected time interval, e.g., 15 sec.

31. ANN Performance During Training

32. ANN Test Data • Driving data from 12 subjects available • 1 subject night session not recorded due to equipment error. • 1 subject morning data not available, software error. • Remaining 10 were used for training ANN and testing results, • NOTE: training data and testing of the ANN were not the same, Testing data selected randomly from the sets not used in the training

33. Performance SLEEP Wake MSE 0.0550 0.0554 NMSE 0.2205 0.2218 MAE 0.1259 0.1245 Min Abs Error 0.0000 0.0000 Max Abs Error 0.9857 0.9806 r 0.8851 0.8840 Percent Correct 92.3000 93.0000 Results Actual Totals Actual Totals Network Output Network Output Wake Wake Sleep Sleep Wake Wake 193 193 179 179 14 14 False Alarm False Alarm Sleep 207 Sleep 207 16 16 191 191 Mis Mis - - classified classified Crash Prediction: All crashes that occurred due to driver falling asleep during the experiment were predicted before the crash occurred.

34. Morning and Night session results

35. Morning and Night session results

36. Morning and Night session results

37. Morning and Night session results

38. Morning and Night session results

39. Time Before Crash When the ANN Generated a first Warning

40. Conclusions • A non-intrusive method of drowsiness detection using steering data is possible • A method using ANN is developed and successfully predicts drowsiness (91% Success Rate) • Method is solely based on driver’s (Vehicle) steering performance • Same method may be applied to detection of fatigue or other related driver performance • Further refining and validation of the algorithm is recommended • Capturing individual driver’s steering while drowsy requires additional research

41. Recommended Additional Research • Additional Simulator Experiments • Validate the Developed Algorithm • Additional Road Conditions • More Diversified Group of Drivers • Road (Experimental) Tests in an Instrumented Vehicle • Further Refining the Algorithm Based on the Road Test Data • Testing of Other Fatigue Related Scenarios • Research on Warning Systems Integrated With This Detection System