DETECTING DRIVER PHONE USE
LEVERAGING CAR
SPEAKERS
Presenter: Ailane Mohamed Toufik
Authors: Jie Yang†, Simon Sidhom†, Gayathri Chandrasekaran∗ , Tam Vu∗ , Hongbo
Liu†,
Nicolae Cecan∗, Yingying Chen†, Marco Gruteser∗, Richard P. Martin∗
†Dept.
of ECE, Stevens Institute of Technology
∗ WINLAB,
Rutgers University
ACM MobiCom 2011
CONTENT
Motivation
 Existing solutions
 System design
 Evaluation
 Conclusion
 Personal critics and paper weaknesses

MOTIVATION
U.S. Department of Transportation – National Highway Traffic
Safety Administration
2.75 people die Every day
Only in 2009
EXISTING SOLUTIONS AND THEIR DRAWBACKS
hands-free devices
Minds off driving.
Cognitive load distract driver!
Real-world accidents indicated that hands-free and handheld users
are as likely to be involved in accidents
EXISTING SOLUTIONS AND THEIR DRAWBACKS

Law

Several States ban handheld phone use
 China / Algeria and many countries as well
Technology
 Hard blocking: radio jammer, blocking phone calls, texting, chat …
 Soft interaction




Routing incoming calls to voicemail,
Delaying incoming text notifications
Automatic reply to callers
Automatic Reply: “I’m driving right
now; will get back with you!”
EXISTING SOLUTIONS AND THEIR DRAWBACKS
The Previous solutions make use of:
GPS
Handover
Signal Strength
Car’s speedometer
38% of automobile trips include passengers
Problem: Only detects if the phone is in a moving
vehicle or not
METHODOLOGY
Detect we
are in a
moving Car
Check if it’s
the Driver
phone
Route
incoming
calls…etc
We will use GPS
Our paper concern
Use existing solution
BASIC IDEA: ACOUSTIC RANGING APPROACH
Acoustic approach based on two assumptions:
 Use the seat location to determine the drivers phone
 Phone is allowed to access the stereo system Bluetooth
Phone connecting with head unit
Symmetric positioning of speakers
Audio
head unit
HOW IT WORKS ?
HOW IT REALLY WORKS ?
∆tij
Propagation Delay
∆Tij = 0 : Phone is
Equidistant from i and j
∆t’ij
∆Tij > 0 : Phone is
Closer to speaker i
∆t’ij
∆Tij < 0 : Phone is
Closer to speaker j
∆t’ij
Speaker i
Speaker j
∆Tij = ∆t’ij - ∆tij
CHALLENGES

Unobtrusiveness

Robustness to noise and multipath

Computation Feasibility on Smartphone
BEEP SIGNAL DESIGN ( HUMAN EAR )

the frequency range of human hearing is generally
considered to be 20 Hz to 20kHz, high frequency
sounds must be much louder to be noticeable
BEEP SIGNAL DESIGN

the current cell phone microphones are more
sensitive to this high-frequency range. We
experimented with an iPhone 3G and an Android
Developer Phone 2 (ADP2).
BEEP SIGNAL DESIGN
16-18kHz  ADP2 phone
 18-20kHz iPhone 3G

BEEP SIGNAL DESIGN




Noise from: Engine, Tire/road and Wind < 1KHz
Conversations range from 300Hz to 3400Hz
Music can range from 50Hz, 15.000Hz which covers
all naturally occurring sound
=> To overcome robustness challenge we choose a
signal above 15Khz
BEEP SIGNAL DESIGN

Length:
 Too
short: a beep is not picked up by the
microphone
 Too long a beep: will add delay to the system
and will be more susceptible to multi-path
distortions.
 We found empirically that a beep length of 400
samples (i.e., 10 ms) represents a good
tradeoff.
ALGORITHM
RANGING AND LOCATION CLASSIFICATION
∆Tij = Sij * f
Sij is the number of samples that the beeps were apart
∆dij = c * ∆ Tij
c is the speed of the sound
∆dij > THlr
THlr is a threshold that could be chosen
as zero / -5cm
f is the sampling frequency (typically 44.1kHz).
(∆dij + ∆dij ) /2 > THfb
∆d13 represent the distance difference from two left side speakers
∆d24 is the distance difference from two right speakers.
EVALUATION -PHONES AND CARSPhones
•Bluetooth radio
•16-bit 44.1kHz sampling rate
•256 RAM
•600 MHz Cortex A8processor
•Bluetooth radio
•16-bit 44.1kHz sampling rate
•192 RAM
•528MHz MSM7200 processor
ADP2
Iphone 3G
Cars
•Bluetooth radio
•Two channel audio system
•two front and two rear speakers
•Interior dimension
Car I: 175 x 183 cm
Car II: 185x 203cm
Honda Civic Si Coupe
Acura sedan
EVALUATION – DIFFERENT SCENARIOS



Testing positions
Different number of occupants
Different noise conditions
 Highway
Driving
60MPH + music playing + w/o window opened
 Phones at front seats only

 Stationary

Varying background noise: idling engine + conversation
EVALUATION RESULTS
Detection Accuracy
EVALUATION RESULTS
Un-calibrated
Calibrated
4 channel, all seats
2 channel, front seats
22
EVALUATION RESULTS
Cup-holder v.s. co-driver left
CONCLUSION

Enabled a first generation system of detecting driver phone use
through a smartphone app
 Practical
today in all cars with built-in Bluetooth
 Leveraging car speakers – without additional hardware

Validated the generality of our approach with two kinds of phones
and in two different cars
 Classification
accuracy of over 90%, and around 95%
with some calibrations

Limitations
 Phone
is muffled by bag or winter coat
 Driver places the phone on an empty passenger seat
PERSONAL CRITIQUES AND FUTURE WORK



Why not fade music to lower the complexity.
Why not testing all positions in all scenarios.
Really, I should use this app ?
FUTURE WORK
Develop a dedicated system with self-calibration
ability.
?

DRIVE
SAFELY
TALK & TEXT
LATER