11-05-2012, 02:41 PM
Nericell - Using Mobile Smartphones for Rich Monitoring of Road and Traffic Conditions
[attachment=21893]
ABSTRACT
We consider the problem of monitoring road and traffic conditions
in a city. Prior work in this area has required the
deployment of dedicated sensors on vehicles and/or on the
roadside, or the tracking of mobile phones by service providers.
Furthermore, prior work has largely focused on the developed
world, with its relatively simple traffic flow patterns.
In fact, traffic flow in cities of the developing regions, which
comprise much of the world, tends to be much more complex
owing to varied road conditions (e.g., potholed roads),
chaotic traffic (e.g., a lot of braking and honking), and a
heterogeneous mix of vehicles (2-wheelers, 3-wheelers, cars,
buses, etc.).
INTRODUCTION
Roads and vehicular traffic are a key part of the day-today
lives of people. Therefore, monitoring their conditions
has received a significant amount of attention. Prior work
([1, 2, 4, 6]) in this area has primarily focused on the developed
world, where good roads and orderly traffic mean
that the traffic conditions on a stretch of road can largely
be characterized by the volume and speed of traffic flowing
through it.
OVERVIEWOF NERICELL
Nericell utilises the sensors on mobile smartphones to detect
braking, bumps and honking in the vicinity. We use
the microphone to capture audio samples for honk detection.
Additionally, we make use of a 3-axis accelerometer
(which we assume to be part of the smartphone) to enable
brake and bump detection. We define a canonical frame
of reference, with the X axis pointing to the front of the
vehicle, the Y axis to the side of the vehicle, and the Z
axis vertically upwards. The measurement reported by the
accelerometer is a function of the force exerted on its sensor
mechanism.
Braking Detection
In general, braking would cause a surge in aX because
the accelerometer would experience a force pushing it to the
front. The surge can be significant even when the brake
is applied at low speed. If a vehicle travelling at 10 kmph
brakes to a halt in 1 second, that would result in an average
of over 0.28g in aX and possibly much larger spikes.
To detect the incidence of braking, we look for the mean of
aX over a sliding window. We signal a braking event when
there is a mean aX of 0.12g over a 4 second time window.
Bump Detection
When a wheel enters a pothole, the wheel descends into
the hole resulting in a sustained dip in the value of aZ until
the wheel hits the bottom of the pothole, which causes a
spike in the value of aZ. At high speeds, the surge in the
value of aZ is very prominent. We found in our experiments
that at low speeds, the surge in the value of aZ is not noticeable;
however, the sustained dip in the values of aZ is
evident.
[attachment=21893]
ABSTRACT
We consider the problem of monitoring road and traffic conditions
in a city. Prior work in this area has required the
deployment of dedicated sensors on vehicles and/or on the
roadside, or the tracking of mobile phones by service providers.
Furthermore, prior work has largely focused on the developed
world, with its relatively simple traffic flow patterns.
In fact, traffic flow in cities of the developing regions, which
comprise much of the world, tends to be much more complex
owing to varied road conditions (e.g., potholed roads),
chaotic traffic (e.g., a lot of braking and honking), and a
heterogeneous mix of vehicles (2-wheelers, 3-wheelers, cars,
buses, etc.).
INTRODUCTION
Roads and vehicular traffic are a key part of the day-today
lives of people. Therefore, monitoring their conditions
has received a significant amount of attention. Prior work
([1, 2, 4, 6]) in this area has primarily focused on the developed
world, where good roads and orderly traffic mean
that the traffic conditions on a stretch of road can largely
be characterized by the volume and speed of traffic flowing
through it.
OVERVIEWOF NERICELL
Nericell utilises the sensors on mobile smartphones to detect
braking, bumps and honking in the vicinity. We use
the microphone to capture audio samples for honk detection.
Additionally, we make use of a 3-axis accelerometer
(which we assume to be part of the smartphone) to enable
brake and bump detection. We define a canonical frame
of reference, with the X axis pointing to the front of the
vehicle, the Y axis to the side of the vehicle, and the Z
axis vertically upwards. The measurement reported by the
accelerometer is a function of the force exerted on its sensor
mechanism.
Braking Detection
In general, braking would cause a surge in aX because
the accelerometer would experience a force pushing it to the
front. The surge can be significant even when the brake
is applied at low speed. If a vehicle travelling at 10 kmph
brakes to a halt in 1 second, that would result in an average
of over 0.28g in aX and possibly much larger spikes.
To detect the incidence of braking, we look for the mean of
aX over a sliding window. We signal a braking event when
there is a mean aX of 0.12g over a 4 second time window.
Bump Detection
When a wheel enters a pothole, the wheel descends into
the hole resulting in a sustained dip in the value of aZ until
the wheel hits the bottom of the pothole, which causes a
spike in the value of aZ. At high speeds, the surge in the
value of aZ is very prominent. We found in our experiments
that at low speeds, the surge in the value of aZ is not noticeable;
however, the sustained dip in the values of aZ is
evident.