Seminar 5520 (Li Li)

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Seminar 5520 (Li Li)

  1. 1. MOBILE PHONE BASED DRUNK DRIVING DETECTION Jiangpeng Dai, Jin Teng, Xiaole Bai, Zhaohui Shen, and Dong Xuan Presented by Li Li
  2. 2. OUTLINE  Problem Definition  Acceleration-Based Drunk Driving Cues  System Design & Implementation  Evaluation  Related Work  Discussion
  3. 3. PROBLEM DEFINITION  What is drunk driving?  Why do we need to use smart phone to detect it?  Requirements of drunk driving monitoring system
  4. 4. ACCELERATION-BASED DRUNK DRIVING CUES  Lateral Acceleration and Lane Position Maintenance
  5. 5. ACCELERATION-BASED DRUNK DRIVING CUES (CONT’D)  Longitudinal Acceleration and Speed Control in Driving Abrupt acceleration or deceleration Erratic braking Jerky stop
  6. 6. SYSTEM DESIGN & IMPLEMENTATION  System Overview
  7. 7. DESIGN OF ALGORITHM  Reading accelerations and angles by using accelerometer and orientation sensor
  8. 8. DESIGN OF ALGORITHM (CONT’D)  Lateral acceleration and longitudinal acceleration detection
  9. 9. LATERAL ACCELERATION PATTERN MATCHING  The pattern matching is to check the variation between the maximum value and the minimum value of Alat within a pattern checking time window WINlat.
  10. 10. LONGITUDINAL ACCELERATION PATTERN MATCHING  When the vehicle acts abnormally in either accelerating or decelerating direction, result in a large absolute value of Alon, making a salient convex or concave shape in its graph of curves.  Set different thresholds for positive Alon and negative Alon.
  11. 11. MULTIPLE ROUND PATTERN MATCHING  Multiple round means that the matching process continues round after round, and the trigger condition is satisfied when several numbers of pattern are recognized.  Multiple round pattern matching will increase the accuracy of drunk driving detection.
  12. 12. IMPLEMENTATION  Drunk driving detection system on Android G1 phone.  Java, with Eclipse and Android 1.6 SDK  Five major components:  User interface  System configuration  Monitoring daemon  Data processing  Alert notification
  13. 13. EVALUATION  Data Collection
  14. 14. EVALUATION (CONT’D)  Detection Performance  False Negative (FN)  False Positive (FP)  Performance Description Abnormal Curvilinear Movements Problems of Speed Control FN Rate (%) 0 0 FP Rate (%) 0.49 2.39 FN Rate (%) (Phone slides) 14.28 0 FP Rate (%) (Phone slides) 1.09 2.72
  15. 15. EVALUATION (CONT’D)  Energy Efficiency
  16. 16. RELATED WORK  GPS  Expensive  Localization error  Energy consuming  Camera  High position requirements  Complicated  Energy consuming for image processing
  17. 17. DISCUSSION  Create another threshold Normal Alert Non-drunk Drunk FP Normal Alert
  18. 18. REFERENCES  Jiangpeng Dai, Jin Teng, Xiaole Bai, Zhaohui Shen and Dong Xuan, Mobile Phone based Drunk Driving Detection, in Proc. of International ICST Conference on Pervasive Computing Technologies for Healthcare (Pervasive Health), March 2010.

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