Presentation is loading. Please wait.

Presentation is loading. Please wait.

By Carl Tenenbaum David Haynes Philip Pham Rachel Wakim.

Similar presentations


Presentation on theme: "By Carl Tenenbaum David Haynes Philip Pham Rachel Wakim."— Presentation transcript:

1 By Carl Tenenbaum David Haynes Philip Pham Rachel Wakim

2 History of Driver Safety  1930s- Seat Belt first introduced  Safety Cage and Padded Dashboard  National Transportation Safety Board  Child’s Booster Seat  Car Crash Testing  Airbag Introduced  NY Enforced Seat Belt Use  Rollover Risk Test

3 Causes of Car Accidents 1. Distracted Drivers (12% was Driver Fatigue) 2. Driver Fatigue 3. Drunk Driving 4. Speeding 5. Aggressive Driving 6. Weather * According to Sixwise.com

4 Driver Fatigue Results  The National Highway Traffic Safety Administration Yearly Statistics  100,000 police-reported crashes  1,550 deaths  71,000 injuries  $12.5 billion in monetary losses. It is difficult to attribute crashes to sleepiness

5

6 To be attractive, a vehicle sensor system should be:  Fairly inexpensive,  Accurate, with a quick response time,  Integrated with the car design, or at least “plug and play”,  Noninvasive,  Discreet, and non-distracting,  Adaptable to different user conditions: i.e., sunglasses, gloves.

7 Head Position Detection  Sense changes in Head Position Tilt  Gives off a warning if the Head Tilt is facing a downward angle. Does Not detect head backwards or turned.  Head Position Down is the Last Stage of Sleep Onset. Usually too late and no warning to Driver.

8 Reed Switch Device

9 Voice Detection  Sense changes in Discrete Voice Parameters such as pitch, frequency, latency and amplitude.  A complex detection algorithm compares normal voice to sample of potential fatigued voice  Can be integrated in GPS or command oriented car systems

10 Voice Channel

11 Types of Voice Sounds  Voiced  Nasal  Fricative  Plosive (Easiest to detect Fatigue)

12 Behavioral Detection  Sense Erratic Driving Behavior  Stores Profile of Person’s Driving Behavior  Compares Profile such as Driver’s Steering and Braking Reaction Time

13 Behaviors Detected  Steering Wheel Angle  Steadiness of Wheel  Lane Departure Proximity  Braking Reaction  Acceleration Reaction

14 Steering Angle Sensors  Use Mechanical (potentiometers) or Optical (contact-free) technologies to collect data or apply correction  Mount on steering shafts  Cover up to 1080 o (3x steering wheel rotations)  Angle resolution of 0.1 o

15 Lane Departure Warning  Use video, laser, and infrared to monitor the lane markings  Activate Vehicle Stability Control (Infiniti), Electric Power Steering (Lexus), etc. to maintain lane position

16 Driving Behavior (Steering Angle)

17 Driving Behavior (Gas Pedal)

18 Driving Behavior (Center Lane Distance)

19 Current Behavioral Sensors  Mercedes E-Class, Volvo, Lexus, Nissan, Infiniti, Volkswagen  Aftermarket- 3Q(2011) AudioVox ($600) *Daimler Chrysler Website

20 Optical Detection  A camera or system of cameras monitor the driver’s facial features for signs of drowsiness.  Computer algorithms analyze blink rate and duration. Infrared LEDs are used to enhance pupil detection.  Yawning and sudden head nods are also detected.

21 Head/eye Camera  Measure head tilting/eye closing/yawning as signs of fatigue or drowsiness.  Non-invasive, no need for user interface.  Can be thwarted by sunglasses or hats. Driver movement may confuse the camera.  1/5 people do not show eye closure as a warning sign. [US Dept. of Transportation]

22 Pupil Detection on Grayscale Image

23 Facial Feature Detection

24 Possible Camera Locations

25 Current Optical Systems  Nap Alarm (LS888)  DD850 Driver Fatigue Monitor

26 Biometric Detection  EKG and EEG  Blood pressure  Skin conductivity (“GSR” – Galvanic Skin Response)  Skin temperature  Breathing rate  Grip force  All shown with correlation to relative drowsiness

27 Electrocardiogram (EKG)  Get information about user’s heart rhythm from at least two electrical contacts on skin.  By removing common mode noise and amplifying the signal, a system can “read” the user’s heart rate, the distance between successive “R” peaks  Drowsiness has been shown to be linked to decreasing heart activity and changes in heart rate variability (HRV)

28 Minimum EKG System  As long as there are at least two contact points, sensor should be able to extract and isolate the signal  Can put these on wheel, seat, or both

29 Wheel sensor  Use sensors on steering wheel to measure skin temperature and conductivity, pulse, etc.  Estimate heart rate variability – can detect drowsiness.  Combines many different metrics to get an overall assessment of the user’s state.  Requires use of both hands, without gloves.

30 Seat sensor  Two pieces of conductive fabric on the driver’s seat (backrest) can take an ECG - measurement. Or on bottom of seat, with wheel as ground (only needs one hand) Needs impedance compensation for the driver’s shirt/coat, etc.

31 Electroencephalogram (EEG)  Use multiple electrodes on scalp to read brain waves  Can very accurately determine sleep/drowsiness stage this way by measuring amplitude/frequency variation of signal  BUT, very invasive

32 Other Possible Sensor Locations  Blood pressure finger cuff on front seat  EKG contacts on left or right armrests  EKG sensors on shifter  Etc.  Or any combination of these.  Theory: the more bio-signs, the better!

33 Wireless wrist monitor  Wristwatch capable of detecting heart rate, skin temperature and conductance.  Example: “Exmovere Empath Watch”:  Transmits via Bluetooth to phone which can signal out; easily extended to cars, many of which already are Bluetooth compatible.  Current design is 3.3” long, 1.7” wide, and 1.3” tall.  Can be bulky, and may not be appealing enough; currently being remodeled [http://www.exmovere.com/healthcare.html]

34 Current Biometric Detection Systems  Currently, there are no systems of these types in commercial use  They all display a high level of accuracy, but their weak point is their invasiveness and unattractiveness  With future work, some of these can be integrated in a behind-the-scenes manner during manufacturing

35

36 Fuzzy Logic Detection More Uncorrelated Sensors Detecting Driver Fatigue Will Increase Detection Probability

37 Corrective and Prevention Actions 1. Elevated Alarms a) Provide Visual Alarm (lights, signs, etc.) b) Provide Audio Alarm (warning tone or voice) c) Recommend short nap (prevent car to start; studies show 15-minute nap increases alertness to 4-5 hours more) 2. Mechanical and Electronic Stimulations a) Counteract to the effects (steering wheel turn, lane drifting, speed change, etc.) b) Apply brake to slow down to safety c) Dispatch for help if no response

38 Corrective Flowchart Actions

39

40 Current Driver Fatigue Products ProductsPriceAccurate Non- InvasiveEffective Overall ScoreCompanyDetection Type Driver Nap Zapper2550%335No NapMotion Nap Alarm (LS888) 50080%566 Leisure Auto SecurityOptical DD850 Driver Fatigue Monitor50080%566Eye AlertOptical Exmovere Empath WristWatch100090%656ExmovereBiometric Driver Assist Package300090%777MercedesBehavioral Undeveloped Market. US Consumer Car GPS Market is $5.1 Billion Market in 2010.

41 Limitations and Future Work  Limitations Probability of Detection Lack of Effective and Timely Alerts Integration of Sensors  Future Work Increase Probability of Detection Use of Multiple Sensors to Increase Probability Develop Effective and Timely Alerts

42 References  [1] “The 6 Most Common Causes of Automobile Crashes(2010)”. Retrieved February 9 th 2011, from tm tm  [2] K. Strohl, J. Blatt, F. Council, K. Georges, J. Kiley, R. Kurrus, A. McCartt, S. Merritt, R.N, A. Pack, S. Rogus, T. Roth, J. Stutts, P. Waller, and D. Willis, “Drowsy Driving and Automobile Crashes” (2010), Retrieved February 21 st 2011, from  [3] What causes Fatigue (2010), Retrieved February 21 st 2011, from  [4] H. Greeley, E. Friets,, J. Wilson, S. Raghavan and J. Berg, “Detecting Fatigue From Voice Using Speech Recognition”, 2006 IEEE International Symposium on Signal Processing and Information Technology  [5] D. Hu, G. Gong, C. Han, Z. Mu, and X. Zhao, “Modeling research on Driver Fatigue”, 2010 International Conference on Computer Application and System Modeling (ICCASM 2010)  [6]L. Bergasa, J. Nuevo, M. Sotelo, R. Barea, and M. Lopez, “Real-Time System for Monitoring Driver Vigilance”, IEEE Transactions on Intelligent Transportation Systems, Vol. 7, no. 1, March 2006  [7] Z. Zhu, Q. Ji, K. Fujimura, and K. Lee, “Combining Kalman Filtering and Mean Shift for Real Time Eye Tracking Under Active IR Illumination”, International Conference on Pattern Recognition, Quebec, Canada, 2002  [8] US Department of Transportation, “An Evaluation of Emerging Driver Fatigue Detection Measures and Technologies”, June 2009  [9] Haisong Gu, Qiang Ji, and Zhiwei Zhu, “Active Facial Tracking for Fatigue Detection” IEEE Workshop on Applications of Computer Vision, Orlando, Florida,  [10]Y. Jie, Y. DaQuan, W. WeiNa, X. XiaoXia, and W. Hui, “Real-Time Detecting System of the Driver’s Fatigue”, 2006  [11]L. Bergasa, J. Nuevo, M. Sotelo, R. Barea, and M. Lopez, “Real-Time System for Monitoring Driver Vigilance”, IEEE Transactions on Intelligent Transportation Systems, vol. 7, no. 1, March, 2006

43 References (Continued)  [12] S. Deshmukh, D. Radake, K. Hande, “Driver Fatigue Detection Using Sensor Network”, International Journal of Engineering Science and Technology, NCICT Conference Special Issue, pp 89-92, February 2011  [13] Y. Tanida, H. Hagiwara, “Simple Estimation of the Falling Asleep Period using the Lorenz Plot for Heart Rate Interval”, JSMBE vol. 44, no. 1, pp , Nov  [14] S. Kar, M. Bhagat, and A. Routray, “EEG signal analysis for the assessment and quantification of driver’s fatigue”, June 2010  [15] L. Servera, M. Fernandez-Chimeno, and M. González, “Study of Sleep Stages By Controlled Inducement and Measurement of Drowsiness Related Biomedical Signals”, 4th International IEEE EMBS Conference on Neural Engineering, April 2009  [16]P. Kithil, R. Jones, and J. MacCuish, “Development of Driver Alertness Detection System Using Overhead Capacitive Sensor Array”, International Driving Symposium on Human Factors in Driver Assessment, Training and Vehicle Design, Aspen, CO,  [17]X. Yu, “Real-time Nonintrusive Detection of Driver Drowsiness”, May 2009  [18] G. Yang, Y. Lin, and P. Bhattacharya, "A driver fatigue recognition model using fusion of multiple features" Systems, Man and Cybernetics, 2005 IEEE International Conference on, vol.2, no., pp Vol. 2, Oct  [19]The John Hopkins university Applied Physics Laboratory “Technologies: Drowsy Driver Detection System”  [20]T. Matsuda and M.Makikawa, “ ECG Monitoring of a Car Driver Using Capacitively-Coupled Electrodes”, 30th Annual International IEEE EMBS Conference,Vancouver, British Columbia, Canada, August 2008  [21]Y. Lin, H. Leng, G. Yang, and H. Cai, “An intelligent noninvasive sensor for driver pulse wave measurement,” IEEE Sensors J., vol. 7, no. 5, pp. 790–799, May  [22] M. Bundele, and R. Banerjee, “Design of Early Fatigue Detection Elements of a Wearable Computing System for the Prevention of Road Accidents”, IEEE,International Society of Automation, Vol 1, pp , 2010

44 References (Continued)  [23]I. Jeong, S. Jun, D. Lee and H. Yoon, “Development of Bio Signal Measurement System for Vehicles”, 2007 International Conference on Convergence Information Technology  [24]Exmovere Holdings Inc, “The New Biotechnological Frontier: The Empath Watch”. Feb  [25] Frost & Sullivan’s, North American GPS Equipment Markets, 2010 (Report A601-22)


Download ppt "By Carl Tenenbaum David Haynes Philip Pham Rachel Wakim."

Similar presentations


Ads by Google