Your SlideShare is downloading. ×
0
Driver                                             Drowsiness and                                             Distraction ...
Overview    Background    Goals           Drowsiness detection           (Distraction detection)    Method           Data ...
Driver Drowsiness detection    Drowsy driving is a road safety problem    - drowsiness contributing in 10-30% of accidents...
Target and Goals                         Sensitivity   Different indicators exist            Specificity       Availabilit...
Data collection                                                       On-road tests were conducted with           Data col...
Test Route Road RV34 Mostly 9 m width Driving lane width 3,75 m Speed limit - mostly 90 km/h Numbers on map are Yearly day...
Ground Truth – KSS             KSS          Description in Swedish                    Verbal description               1  ...
Example indicators of driver sleepiness                                                                                   ...
Video examples                                       Video examplesFordonsstrategisk Forskning och Innovation       FFI – ...
Sensor fusion                   SVM (Support Vector Machine):                   Machine learning method using data from fi...
Evaluation Method  Assuming a binary classification,                 A  alert or =sensitivitydrowsy               A+C     ...
Example of results from sensor fusion      Model                             Fitness                  Sens                ...
Fulfillment of goals     The fusion algorithm shall show an improvement in:       - Improved performance                  ...
Summary    Controlled experiment on public roads    43 drivers so far    What is ideal performance?           Method devel...
Thank you for you attention!Fordonsstrategisk Forskning och Innovation              FFI – D4SFALR-JKAR/Jan2011/Transportfo...
Upcoming SlideShare
Loading in...5
×

Session 48 Johan Karlsson

301

Published on

Published in: Education, Business
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
301
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
4
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Transcript of "Session 48 Johan Karlsson"

  1. 1. Driver Drowsiness and Distraction Detection by Sensor Fusion D4SF Johan Karlsson, Autoliv Transportforum 2011Fordonsstrategisk Forskning och Innovation FFI – D4SFALR-JKAR/Jan2011/Transportforum - 1 Copyright Autoliv Inc., All Rights Reserved
  2. 2. Overview Background Goals Drowsiness detection (Distraction detection) Method Data collection Training/optimization of classifier Sensor fusion Results Reference – ground truth Improvement by (f)using parallel detectorsFordonsstrategisk Forskning och Innovation FFI – D4SFALR-JKAR/Jan2011/Transportforum - 2 Copyright Autoliv Inc., All Rights Reserved
  3. 3. Driver Drowsiness detection Drowsy driving is a road safety problem - drowsiness contributing in 10-30% of accidents (Anund & Patten 2010) What can be done? Commercial fleet traffic Fatigue Risk Management Work time regulation Detection and warning Privately owned vehicles Detection and warning Detection? Detection systems offered as option from several OEMs So far, performance is far from ideal...Fordonsstrategisk Forskning och Innovation FFI – D4SFALR-JKAR/Jan2011/Transportforum - 3 Copyright Autoliv Inc., All Rights Reserved
  4. 4. Target and Goals Sensitivity Different indicators exist Specificity Availability - ’Physiology’ A Indicator measures+ blink duration etc. - – + - Driving performance measures - lane keeping measures - EnvironmentBmeasures - time of day, traffic, road type Indicator – (previous sleep possible in commercial fleet vehicles??) + + Indicator C + + – Various indicators have different strengths and weaknesses Improve performance by fusing data from multiple indicators+ Fusion ++ ++ + The fusion algorithm shall show an improvement in: - Overall performance - Reduced number of faulty detections - Increased number of correct detectionsFordonsstrategisk Forskning och Innovation FFI – D4SFALR-JKAR/Jan2011/Transportforum - 4 Copyright Autoliv Inc., All Rights Reserved
  5. 5. Data collection On-road tests were conducted with Data collection governmental approval (N2007/5326/TR) and ethical approval by Regional ethics Relevant vehicle data approval board (EPN 142-07 T34-09). Speed, lane position, SW angle, pedals etc. Video based gaze direction, eyelid opening, head pos KSS value every 5 minute EEG, EOG and EMG Video recordings (road scenery and cabin) In total: 43 drivers have completed 3 drives each Procedure: Each driver drove three times during one day (day, evening and night). Trip duration 80-90 minutesFordonsstrategisk Forskning och Innovation FFI – D4SFALR-JKAR/Jan2011/Transportforum - 5 Copyright Autoliv Inc., All Rights Reserved
  6. 6. Test Route Road RV34 Mostly 9 m width Driving lane width 3,75 m Speed limit - mostly 90 km/h Numbers on map are Yearly day traffic volume in January 2002 We know of only a few similar studies performed on public roads 90 minute driving, approx 115 km distance Rested safety driver – dual commandFordonsstrategisk Forskning och Innovation FFI – D4SFALR-JKAR/Jan2011/Transportforum - 6 Copyright Autoliv Inc., All Rights Reserved
  7. 7. Ground Truth – KSS KSS Description in Swedish Verbal description 1 extremt pigg extremely alert 2 mycket pigg very alert 3 pigg alert 4 ganska pigg rather alert 5 varken pigg eller sömnig neither alert nor sleepy 6 första tecknen på sömnighet some signs of sleepiness 7 sömnig, ej ansträngande vara vaken sleepy, but no effort to keep alert 8 sömnig, viss ansträngning vara vaken sleepy, some effort to keep alert 9 mycket sömnig, ansträngande vara vaken, very sleepy, great effort to keep alert, kämpar mot sömnen fighting sleep + Simple to collect + Simple to understand – immediately ready for analysis - Training needed for participants - Some offset for inexperienced participants?Fordonsstrategisk Forskning och Innovation FFI – D4SFALR-JKAR/Jan2011/Transportforum - 7 Copyright Autoliv Inc., All Rights Reserved
  8. 8. Example indicators of driver sleepiness Closing Opening Open 200 ms Closed Blink duration (AS): Amplitude 400 ms Closed Mean blink duration Short Blink Long Blink GVI (Sandberg 2008) Lane keeping variability (Lane): G= 1 N ∑ w(zi ) | zi |k N i=1 Variability in Steering wheel position or zi = xi − (δ x + (1 − δ ) p) Lane Position. e.g. using Generic cL cR Variability Indicator (Sandberg 2008) . w( z) = −α L ( z −β L ) + −α R ( z −β R ) 1+ e 1+ e Time-of-day (TPM): Expected drowsiness with regard to time of day (circadian rythm) * Each indicators has several parameters that needs to be tuned for optimal performanceFordonsstrategisk Forskning och Innovation FFI – D4SFALR-JKAR/Jan2011/Transportforum - 8 Copyright Autoliv Inc., All Rights Reserved
  9. 9. Video examples Video examplesFordonsstrategisk Forskning och Innovation FFI – D4SFALR-JKAR/Jan2011/Transportforum - 9 Copyright Autoliv Inc., All Rights Reserved
  10. 10. Sensor fusion SVM (Support Vector Machine): Machine learning method using data from field tests to calculate “best fit” function between indicator values and ground truth (KSS rating scale) Indicator parameters optimized simultaneously with training of SVM Data sets for SVM training and validation are from separate drivers. Thus, validation is done on truly “never-before-seen” data. Drowsy data Goal: Find the maximum margin hyperplane Indicator B Alert data Indicator AFordonsstrategisk Forskning och Innovation FFI – D4SFALR-JKAR/Jan2011/Transportforum - 10 Copyright Autoliv Inc., All Rights Reserved
  11. 11. Evaluation Method Assuming a binary classification, A alert or =sensitivitydrowsy A+C Ground truth Performance is the mean value of D Non-specificity = sensitivity and specificity Drowsy Performance + D Drowsy B is related to the Algorithm output A B sensitivity + specificity proportion of the time where the Detectperformancis = algorithm e correct (hit) (false hit) 2 Non- C D Detect (miss) (correct reject) KSS = ground truth Ground truth cutoff Sum A+C B+D Binary Algo outputKSS Time Fordonsstrategisk Forskning och Innovation FFI – D4SF ALR-JKAR/Jan2011/Transportforum - 13 Copyright Autoliv Inc., All Rights Reserved
  12. 12. Example of results from sensor fusion Model Fitness Sens Spec Blink 0.66 (0.64) 0.36 (0.32) 0.96 (0.95) Blink + Circadian 0.80 (0.83) 0.77 (0.79) 0.83 (0.87) Blink + Lane + Circ. 0.80 (0.78) 0.68 (0.68) 0.92 (0.88) Blink + Steer + Circ. 0.80 (0.85) 0.76 (0.81) 0.84 (0.89) First figure is training data performance second figure is test data performance Decision every 1 minute KSS >= 7 drowsy KSS < 7 alertFordonsstrategisk Forskning och Innovation FFI – D4SFALR-JKAR/Jan2011/Transportforum - 14 Copyright Autoliv Inc., All Rights Reserved
  13. 13. Fulfillment of goals The fusion algorithm shall show an improvement in: - Improved performance true - Increased number of correct detections true - Reduced number of faulty detections (?)Clearly improved overall performance – Minor differences between different combinationsFordonsstrategisk Forskning och Innovation FFI – D4SFALR-JKAR/Jan2011/Transportforum - 15 Copyright Autoliv Inc., All Rights Reserved
  14. 14. Summary Controlled experiment on public roads 43 drivers so far What is ideal performance? Method developed with focus on mathematical performance Most important goal is to have relevant warnings More data is needed: Different road types Different conditions (weather, drive duration etc.) Different driver types (age, cultural differences etc.)Fordonsstrategisk Forskning och Innovation FFI – D4SFALR-JKAR/Jan2011/Transportforum - 16 Copyright Autoliv Inc., All Rights Reserved
  15. 15. Thank you for you attention!Fordonsstrategisk Forskning och Innovation FFI – D4SFALR-JKAR/Jan2011/Transportforum - 17 Copyright Autoliv Inc., All Rights Reserved
  1. A particular slide catching your eye?

    Clipping is a handy way to collect important slides you want to go back to later.

×