by carl tenenbaum david haynes philip pham rachel wakim l.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Integrated Sensor Technologies Preventing Accidents Due to Driver Fatigue PowerPoint Presentation
Download Presentation
Integrated Sensor Technologies Preventing Accidents Due to Driver Fatigue

Loading in 2 Seconds...

play fullscreen
1 / 26

Integrated Sensor Technologies Preventing Accidents Due to Driver Fatigue - PowerPoint PPT Presentation


  • 146 Views
  • Uploaded on

By Carl Tenenbaum David Haynes Philip Pham Rachel Wakim. Integrated Sensor Technologies Preventing Accidents Due to Driver Fatigue. Us Vehicle Deaths. Roughly 100 Deaths per Day. Causes of Car Accidents. Distracted Drivers (12% was Driver Fatigue) Driver Fatigue Drunk Driving

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Integrated Sensor Technologies Preventing Accidents Due to Driver Fatigue' - hoai


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
by carl tenenbaum david haynes philip pham rachel wakim
By

Carl Tenenbaum

David Haynes

Philip Pham

Rachel Wakim

Integrated Sensor Technologies Preventing Accidents Due to Driver Fatigue

us vehicle deaths
Us Vehicle Deaths

Roughly 100 Deaths per Day

causes of car accidents
Causes of Car Accidents
  • Distracted Drivers (12% was Driver Fatigue)
  • Driver Fatigue
  • Drunk Driving
  • Speeding
  • Aggressive Driving
  • Weather

* According to Sixwise.com

driver fatigue results
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

head position detection
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.
behavioral detection
Behavioral Detection
  • Sense Erratic Driving Behavior
  • Stores Profile of Person’s Driving Behavior
  • Compares Profile as Driver’s Steering and Braking Reaction Time
voice detection
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
optical detection
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.
biometric detection
Biometric Detection
  • Capacitive Array on vehicle ceiling detects changes in driver’s body position.
  • Sensors placed on steering wheel, seat, or wristwatch device monitor driver’s vital signs for analysis.
  • Low power Doppler radar monitors vital signs and body position for analysis.
  • Artificial neural network software analyzes steering wheel behavior for indicators of fatigue.
to be attractive a vehicle sensor system should be
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.
head eye camera
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]
wheel sensor
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.
seat sensor
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.
  • Needs impedance compensation for the driver’s shirt/coat, etc.
wireless wrist monitor
Wireless wrist monitor
  • Wristwatch capable of detecting heart rate, skin temperature and conductance.
  • Example: “ExmovareEmpath 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 undergoing remodeling

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

*Daimler Chrysler Website

technology
Technology
  • Audio & Video Warning Circuits
  • Starter-Disabled Circuit
  • Auto-Pilot Control
  • Communication Protocol
current driver fatigue products
Current Driver Fatigue Products

Undeveloped Market. US Consumer Car GPS Market is $5.1 Billion Market in 2010.

references
References
  • 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 2007.
  • X. Yu, “Real-time Nonintrusive Detection of Driver Drowsiness”, May 2009
  • US Department of Transportation, “An Evaluation of Emerging Driver Fatigue Detection Measures and Technologies”, June 2009
  • Y. Jie, Y. DaQuan, W. WeiNa, X. XiaoXia, and W. Hui, “Real-Time Detecting System
  • of the Driver’s Fatigue”, 2006
  • Exmovere Holdings Inc, “The New Biotechnological Frontier: The Empath Watch”. Feb. 2011 http://www.exmovere.com/pdf/Exmovere_Wearable_Sensor_Research.pdf
  • S. Kar, M. Bhagat, and A. Routray, “EEG signal analysis for the assessment and quantification of driver’s fatigue”, June 2010
  • The 6 Most Common Causes of Automobile Crashes(2010). Retrieved February 9th 2011, from http://www.sixwise.com/newsletters/05/07/20/the_6_most_common_causes_of_automobile_crashes.htm
  • What causes Fatigue (2010), Retrieved February 21st 2011, from http://unsafetrucks.org/driver_fatigue.htm
  • Kingman P. Strohl, M.D, Jesse Blatt, Ph.D, Forrest Council, Ph.D, Kate Georges, James Kiley, Ph.D, Roger Kurrus, Anne T. McCartt, Ph.D, Sharon L. Merritt, Ed.D., R.N, Allan I. Pack, Ph.D., M.D, Susan Rogus, R.N., M.S., Thomas Roth, Ph.D, Jane Stutts, Ph.D, Pat Waller, Ph.D., David Willis, “Drowsy Driving and Automobile Crashes” (2010), Retrieved February 21st 2011, from http://www.nhtsa.gov/people/injury/drowsy_driving1/drowsy.html#NCSDR/NHTSA
references continue
References Continue
  • Toshiyuki Matsuda, Masaaki Makikawa, “ ECG Monitoring of a Car Driver Using Capacitively-Coupled Electrodes” 30th Annual International IEEE EMBS Conference ,Vancouver, British Columbia, Canada, August 20-24, 2008
  • Luis M. Bergasa, Jesús Nuevo, Miguel A. Sotelo, Rafael Barea, and María Elena Lopez “Real-Time System for Monitoring Driver Vigilance” IEEE Transactions on Intelligent Transportation Systems, vol. 7, no. 1, March, 2006
  • The John Hopkins university Applied Physics Laboratory “Technologies: Drowsy Driver Detection System” http://www.jhuapl.edu/ott/technologies/featuredtech/DDDS/
  • George Washington University Center for Intelligent Systems Research “Driver Assistance: Drowsy/Fatigued Driver Detection” http://www.cisr.gwu.edu/research/drowsy_details.html
  • EURASIP Journal on Advances in Signal Processing Volume 2010 (2010), Article ID 438205 “Driver Drowsiness Warning System Using Visual Information for Both Diurnal and Nocturnal Illumination Conditions” http://www.hindawi.com/journals/asp/2010/438205/
  • Jennifer F. May, Carryl L. Baldwin Transportation, “Driver fatigue: The importance of identifying causal factors of fatigue when considering detection and countermeasure technologies”, Research Part F 12 (2009) 218–224
  • H.P. Greeley, E. Friets,, J.P. Wilson, S. Raghavan and J. Picone J. Berg, “Detecting Fatigue From Voice Using Speech Recognition”, 2006 IEEE International Symposium on Signal Processing and Information Technology