1. MOBILE PHONE BASED DRUNK
DRIVING DETECTION
Jiangpeng Dai, Jin Teng, Xiaole Bai,
Zhaohui Shen, and Dong Xuan
Presented by
Li Li

2. OUTLINE
 Problem Definition
 Acceleration-Based Drunk Driving Cues
 System Design & Implementation
 Evaluation
 Related Work
 Discussion

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. ACCELERATION-BASED DRUNK DRIVING
CUES
 Lateral Acceleration and Lane Position Maintenance

5. ACCELERATION-BASED DRUNK DRIVING
CUES (CONT’D)
 Longitudinal Acceleration and Speed Control in Driving
Abrupt acceleration or deceleration
Erratic braking
Jerky stop

6. SYSTEM DESIGN & IMPLEMENTATION
 System Overview

7. DESIGN OF ALGORITHM
 Reading accelerations and angles by using
accelerometer and orientation sensor

8. DESIGN OF ALGORITHM (CONT’D)
 Lateral acceleration and longitudinal acceleration
detection

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. 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. 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. 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. EVALUATION
 Data Collection

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. EVALUATION (CONT’D)
 Energy Efficiency

16. RELATED WORK
 GPS
 Expensive
 Localization error
 Energy consuming
 Camera
 High position requirements
 Complicated
 Energy consuming for image processing

17. DISCUSSION
 Create another threshold
Normal Alert
Non-drunk Drunk
FP
Normal Alert

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.