Seminar 5520 (Li Li)

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!