previous blog, we discussed the basic concepts behind vehicle detection using
magnetometers. This week, we will discuss more about the ideal sensor
placements and some of the hurdles faced with vehicle detection accuracy.
The intensity of the measured flux line density, or field strength, decreases
with an increase in distance between the sensor and the vehicle. This would
result in a very weak signal that is hard for event detection and analysis.
So, the effective placement of the sensor relative to the car surface is an
important point to consider.
Mounting the sensor on the road (near the surface) is the most common and
effective method, as shown in figure 1. It is used to detect vehicle presence
as the underneath of the car has the highest metallic concentration. The next
ideal location could be on top of a 2.5 foot pole at the side of the lane with
an optimal distance between the pole and the moving vehicle of 1 to 2 feet.
From the corresponding signature profiles, we can see that placing the sensor
on the road surface obtains a signal profile with higher magnetic field
strength change. Figure 2 shows how increasing the distance between the sensor
and car will have impact on the measured signal strength.
Hurdles towards vehicle detection
OK! Now that we know
the basic concept around vehicle sensing, let’s use a simple threshold
based detection method to detect vehicle presence. i.e. if the RMS value (all
X, Y and Z axis) of the measured signal is greater than a user defined
threshold, then a vehicle is determined to be present/passed and vice versa.
However though this may seem pretty straightforward, in certain critical
scenarios it might produce a false positive/false negative trigger.
Considering the class of vehicles according to size- ranging from bikes to
trucks- the amount of ferrous metal interacting with the earth’s field
would vary. Therefore, the measured change in signal strength would vary.
Hence a common threshold of activation would not be a good solution to detect
all classes of vehicles.
Also the sensor’s baseline signal (i.e. under zero flux field) varies
slightly with temperature. This change in offset, due to temperature change,
might lead to false triggers. Hence instead of just using threshold based
detection, pattern recognition techniques can be used to understand the
signature. This will help obtain meaningful time or frequency domain features
like mean, standard deviation, correlation between various axes, min/max
values etc. Initially, a trained model can be built by obtaining signatures of
different class of vehicles (according to size). The efficiency of this
trained pattern recognition model will depend on the size of the training set.
Thus, irrespective of the operating conditions, the pattern recognition model
will help us identify vehicle presence effectively by closely analyzing the
Another common and obvious hurdle is how to prevent false triggers in a
scenario as shown in figure 3a. Will sensor 2 sense vehicle No.1‘s
presence? How do we account for it and prevent false triggers from adjacent
We can place the sensors at the extremities of the lanes as shown in the
figure 3b. But it’s quite tricky for a multi-lane road. So, one quick
way is to magnetically shield the rear part of the sensors (in this case
sensor2), this would reduce the amplitude of the field strength caused by
Vehicle 1 on sensor 2. So, if the amplitude of signal change measured by
sensor 1 (as the vehicle passes through) is greater than the signal change
measured by sensor2, then the car is actually in lane 1 rather than 2.
In the next series we can identify some of the potential use cases and
applications apart from mere vehicle detection using magnetometers.
In the meantime, please visit
to learn more about our magnetometer offering.