31-01-2013, 04:36 PM
Mobile Phone Based Drunk Driving Detection
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Abstract
Drunk driving, or officially Driving Under the Influence
(DUI) of alcohol, is a major cause of traffic accidents
throughout the world. In this paper, we propose a highly efficient
system aimed at early detection and alert of dangerous vehicle
maneuvers typically related to drunk driving. The entire solution
requires only a mobile phone placed in vehicle and with
accelerometer and orientation sensor. A program installed on the
mobile phone computes accelerations based on sensor readings,
and compares them with typical drunk driving patterns extracted
from real driving tests. Once any evidence of drunk driving is
present, the mobile phone will automatically alert the driver or
call the police for help well before accident actually happens. We
implement the detection system on Android G1 phone and have it
tested with different kinds of driving behaviors. The results show
that the system achieves high accuracy and energy efficiency.
INTRODUCTION
Motivation
Crashes caused by impairment of alertness in vehicle drivers
pose a serious danger to people, not only to drivers themselves
but also often to the general public [1]. According to the
report of U.S. National Highway Traffic Safety Administration
(NHTSA), more than a million people have died in traffic
crashes in the United States since 1966. During these tragedies,
drunk driving is one of the main causes. The concern related to
drunk driving is not only the high crash rate, but also the type
of crashes that are most likely to happen. In the last two years,
2007 and 2008, 13,041 and 11,773 alcohol-impaired driving
fatalities happened, respectively. Both are 32% of the total
fatalities of that year [2]. During these crashes, ten of thousands
of people were killed, and much more people injured. Besides
being a great threat to public safety and health, drunk driving
also imposes a heavy financial burden on the whole society,
especially on the healthcare sector. According to U.S. Central
of Disease control (CDC) [3], the annual cost of alcoholrelated
crashes totals more than $51 billion in 2008. Lee et
al. pointed out in their work [4] that the emergency department
spends $4; 538 more on average in treating alcohol-impaired
motor vehicle crash victims, especially for patients who are
minimally injured, because of their impaired reasoning and
blunted sensation
Our Contributions
In this paper, we propose utilizing mobile phones as the platform
for drunk driving detection system development, as they
naturally combine the detection and communication functions.
To the best of our knowledge, we are the first to do so.
As a self-contained device, mobile phone presents a mature
hardware and software environment for the development of
active drunk driving monitoring system. The system based
on mobile phone can function effectively on its own because
mobile phones are highly portable, all necessary components
are already integrated therein.
RELATED WORK
There are some existing research on the development and
validation of technological tools for driving monitoring. Some
of them are known under the name of driver vigilance monitoring,
and they focus on monitoring and preventing driver fatigue.
Other work focus on real-time driving pattern recognition. In
detail, they use various methodologies and techniques described
as follows.
Visual observation is an option to detect driver fatigue. In
[10], Zhu et al. have used two cameras on dashboard to capture
the visual cues of drivers, such as eyelid movement, gaze
movement, head movement and facial expression, in order to
predict fatigue with a probabilistic model. In [11], Albu et
al. have conducted the research in a relatively simpler way.
They claim the sleep onset is the most critical consequence
of fatigued driving, separate the issue of sleep onset from the
global analysis of the physiological state of fatigue, and take
eyes opening and closing as cues of sleep onset. They have
used vision-based system to monitor the eyes conditions in
order to detect fatigue in driving. In [12], Lee et al. have used
two fixed cameras to capture the driver’s sight line and the
driving lane path for the purpose of driving pattern and status
recognition. They calculate the correlation coefficients among
them to monitor the driving status and patterns. These methods
all need one or more cameras to be installed in the vehicle and
just in front of the driver. It will cause certain potential safety
hazard to the driver.
System Overview
The drunk driving detection system is made up of four
components, as presented in Fig. 2. They are (1) monitoring
daemon module, (2) calibration module, (3) data processing
and pattern matching module and (4) alert module. The third
module implements the detection algorithm, as marked by a
dashed box. Our design is general, not constrained to any
particular brand or type of mobile phone. And our design is also
power-aware, as hardware such as the screen is only activated
when necessary.
The work flow of our drunk driving detection system is
also illustrated in Fig. 2. After the system starts manually, a
calibration procedure is conducted when the system detects
that the phone is located in a moving vehicle. Then the
main program launches, working as a background daemon.
The daemon monitors the driving behaviors in real time and
collects acceleration information. The collected information includes
lateral and longitudinal acceleration. They are processed
separately, and used as inputs to the multiple round pattern
matching process. At the same time, the historical information
will be registered. This information is helpful in the following
round pattern matching process. If the pattern condition is
satisfied, which means a drunk driving is detected, one signal
is transmitted to trigger an alert. The phone may alarm to
remind the driver or automatically contact the police for help.
If the condition is not satisfied, execution returns to the daemon
immediately. In the following sections, we will present the
details of algorithm design.
CONCLUSION
In this paper, we present a highly efficient mobile phone
based drunk driving detection system. The mobile phone, which
is placed in the vehicle, collects and analyzes the data from its
accelerometer and orientation sensor to detect any abnormal or
dangerous driving maneuvers typically related to driving under
alcohol influence. Experiments show that our solution sees very
low false negative and false positive rates.