seminar 5520 (li li)
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TRANSCRIPT
MOBILE PHONE BASED DRUNK DRIVING DETECTION
Jiangpeng Dai, Jin Teng, Xiaole Bai,
Zhaohui Shen, and Dong Xuan
Presented by
Li Li
OUTLINE
Problem Definition
Acceleration-Based Drunk Driving Cues
System Design & Implementation
Evaluation
Related Work
Discussion
PROBLEM DEFINITION
What is drunk driving? Why do we need to use smart phone to detect it? Requirements of drunk driving monitoring system
ACCELERATION-BASED DRUNK DRIVING CUES Lateral Acceleration and Lane Position Maintenance
ACCELERATION-BASED DRUNK DRIVING CUES (CONT’D)
Longitudinal Acceleration and Speed Control in
Driving
Abrupt acceleration or deceleration
Erratic braking
Jerky stop
SYSTEM DESIGN & IMPLEMENTATION
System Overview
DESIGN OF ALGORITHM
Reading accelerations and angles by using accelerometer and orientation sensor
DESIGN OF ALGORITHM (CONT’D)
Lateral acceleration and longitudinal acceleration detection
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.
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.
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.
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
EVALUATION
Data Collection
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
EVALUATION (CONT’D)
Energy Efficiency
RELATED WORK
GPS Expensive Localization error Energy consuming
Camera High position requirements Complicated Energy consuming for image processing
DISCUSSION
Create another threshold
Normal Alert
Non-drunk Drunk
FPNormal Alert
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.
Thank You!