The document describes tests conducted with a 2006 Subaru Outback to collect data on speed, acceleration, time and other metrics using a Motec C125 device. An imaginary racing track was traced in a parking lot and laps were run at speeds of 5, 10 and 20 mph. Graphs of the data show that higher speeds resulted in greater lateral acceleration and yaw angles as the car handled corners. While the data provided insights, its accuracy was limited by the improvised track and lack of driver experience. Recommendations include using a calibrated measurement device and practicing laps before collecting data.

1. University of Colorado - Denver
Simulating a racing track and gathering data
Team: Diane’s team
Lucas Gimenes de Almeida
11/17/2014

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Introduction
The group performed tests with a 2006 Subaru Outback 2.5i wagon with the
objective to collect data generated through the Motec C125. An imaginary track was
traced in a parking lot and speeds of 5 mph, 10 mph, and 20 mph were set to be
performed during the tests. The objective of these tests was to check how the speed
would affect the accelerations, lap time, throttle position and the general performance of
the car.
Material and Methods
The measurement device used for the tests was the Motec C125 and it was
plugged to the car for data gathering.
For a better data collection, 3 laps were set to each speed and it should start
from the lower to the higher. This was done because since the group was not able to
use a proper racing track, it was necessary to imagine one in the parking lot and to race
on it. From these laps, speed, GPS (position), time, longitudinal, vertical and lateral
accelerations and yaw sensor data were collected. In addition, unfortunately, the Motec
should have read the throttle position, but for some unknown reason it did not.
Results and Discussion
For the following graphs related to GPS, the Motec generated the data of latitude
and longitude with units of degrees and altitude in meters. To overcome this issue for
the plotting, a conversion factor of 111,133 km/degree was used. This conversion was
supposed to be used only for longitude conversion, but since the variation of degrees in
latitude was small, so it was adopted for its conversion too.
The first speed to be tested was 5 mph, because the imaginary track would be
better traced with a lower speed. A 3-D graph was plotted using Matlab and it is
possible to see the laps overlaid, and how the driving was performed.

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Figure 1: Overlaid laps for the 5 mph speed
This procedure was done for the 10 and 20 mph speeds as is shown below.
Figure 2: Overlaid laps for the 10 mph speed

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Figure 3: Overlaid laps for the 20 mph speed
Through the figure 1 is possible to see that the track was well-defined and
although the lap 3 is a little misplaced related to the others, I still a very consistent track.
In figure 2, the track still well-defined, but in figure 3 is now possible to see some
discrepancies on the curves since with a higher speed, the car handling gets more
difficult.
For a better analysis of the track during each speed, the last lap of each one was
overlaid.
Figure 4: Last lap of each speed overlaid

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In the figure 4, if compared the track between the speeds, the straights have a
slight difference due the way each curve was done in the test. For the 5 and 10 mph,
the curves do not get to open because these speed permit to handle the car better
during them. On the other hand, for 20 mph the handling becomes harder and the
curves get more open as is possible between (4:6 latitude; -6:-10 longitude) and (-4:-6
latitude; -2:2 longitude).
Since the next graphs were plotted in function of distance, a table with the
distances and time of each lap was built.
Table 1: Distances and time of each lap at 5 mph
Lap Distance (m) Time (s)
1 328 67
2 346 69
3 327 67
Table 2: Distances and time of each lap at 10 mph
Lap Distance (m) Time (s)
1 331 57
2 334 56
3 332 56
Table 3: Distances and time of each lap at 20 mph
Lap Distance (m) Time (s)
1 383 46
2 318 37
3 331 38
When the tests were being made, speeds were developed through them and for
a better analysis to what was happening during straights and curves, graphs of speed
per time were plotted.

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Figure 5: Speed per time at 5 mph
Figure 6: Speed per time at 10 mph

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Figure 7: Speed per time at 20 mph
From the graphs above is noticed that at peaks, the car was being accelerated
and at the valleys, decelerated. Analyzing that, is possible to conclude at what distance
the cornering, the exit from the curve and acceleration resumption occurred. In addition,
the data was not much accurate because was not fixed properly and the pilot was not
professional and had no experience with the track.
The next graphs plotted were longitudinal, lateral, and vertical acceleration and
yaw sensor.
The first graphs are related to lateral acceleration. The lateral acceleration is the
acceleration created when a vehicle corners that tends to push a vehicle sideways

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Figure 8: Lateral acceleration per time at 5 mph
Figure 9: Lateral acceleration per time at 10 mph

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Figure 10: Lateral acceleration per time at 20 mph
How the graphs were too polluted with the data and it was a little difficult to
understand the data, an average was calculated and the following graph was obtained.
With this average, a better comparison could be done.
Figure 11: Average lateral acceleration of all speeds
From the graph of the averages, we can notice that for every valley of the lateral
acceleration, the car was cornering and that at the speed of 20 mph the lateral

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acceleration was greater if compared to the other speeds, since the formula is
represented by:
𝑎 =
𝑉2
𝑅
Where R is the curvature radius and for this experiment can be considered constant and
V is speed. So, at a higher speed, a higher lateral acceleration will be obtained.
The vertical acceleration is the acceleration resulted from gravity and the
following graphs were plotted for each speed.
Figure 12: Vertical acceleration per time at 5 mph
Figure 13: Vertical acceleration per time at 10 mph

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Figure 14: Vertical acceleration per time at 20 mph
How the graphs were too polluted with the data and it was a little difficult to
understand the data, an average was calculated and the following graph was obtained.
With this average, a better comparison could be done.
Figure 15: Average vertical acceleration of all speeds
Since the tests were performed in a flat parking lot, so the altitude did not vary
and the latitude varied slightly, therefore the vertical acceleration does not vary much,
because it depends of altitude related to the ocean level and latitude on Earth.

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The next data that will be analyzed is the longitudinal acceleration. This
acceleration is related to the acceleration generated by the engine and the braking
generated by the brakes (deceleration).
Figure 16: Longitudinal acceleration per time at 5 mph
Figure 17: Longitudinal acceleration per time at 10 mph

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Figure 18: Longitudinal acceleration per time at 20 mph
How the graphs were too polluted with the data and it was a little difficult to
understand the data, an average was calculated and the following graph was obtained.
With this average, a better comparison could be done.
Figure 19: Average longitudinal acceleration of all speeds

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From the average, it is possible to analyze that the greater longitudinal
acceleration was developed at 20 mph, since with this speed it was possible to cover
more track than the others. The increase of longitudinal acceleration is generated due to
the acceleration of the car, so we can conclude that it was developed on a straight line.
On the other hand, the decrease of this acceleration means that was occurring the
braking and the deceleration during the preparation for the cornering and the cornering.
Furthermore, either the data from the yaw was analyzed. The yaw sensor is a
device which measures the car rolling angle related to its vertical axis. This rolling
happens due to the lateral acceleration and when this one is higher, greater will be the
rolling angle. Intending to analyze this angle, the following graphs were plotted.
Figure 20: Yaw sensor data per time at 5 mph
Figure 21: Yaw sensor data per time at 10 mph

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Figure 22: Yaw sensor data per time at 20 mph
If we take the points out of curve, is noticed that when the speed increases, the
yaw angle increases, because the lateral acceleration influences directly on this angle
and the lateral acceleration also depends of the speed. Therefore for a higher, greater
the lateral acceleration and greater the yaw angle.
Conclusion
After analyzing all the collected data, is concluded that the car acceleration can
vary the other accelerations related to it, and therefore any data related to this other
data.
Another aspect to outline is how at higher speeds the car tends to roll outward.
This was analyzed through the yaw sensor which showed that the angle increases due
to the lateral acceleration which depends of the speed.
In general the data was not much accurate since the Motec was not fixed at
some place in the car and since we did not have a professional driver and optimized
track, they did not help as well.
Recommendations
 For a better accurate data, try to fix the Motec somewhere in the car and
calibrate it as well.
 Practice the laps a few time before start gathering data and do not race
with ice on the track.
 Try to mark a decent track that can be performed.

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 Check if the measurement device is working well, so any data will not be
missed.
Appendix A
plot(x1,y1)
hold on
plot(x2,y2,'r')
hold on
plot(x3,y3,'m')
xlabel('Time (s)')
ylabel('Lateral acceleration (G)')
legend('lap 3','lap 2','lap 1')
plot (x1,y1)
hold on
plot (x2,y2,'r')
hold on
plot (x3,y3,'m')
legend ('20 mph','10 mph','5 mph')
xlabel('Distance (m)')
ylabel('Lateral acceleration (G)')
plot (x1,y1)
hold on
plot (x2,y2,'r')
hold on
plot (x3,y3,'m')
legend ('20 mph','10 mph','5 mph')
xlabel('Distance (m)')
ylabel('Longitudinal acceleration (G)')
plot(x1,y1)
hold on
plot(x2,y2,'r')
hold on
plot(x3,y3,'m')
xlabel('Time (s)')
ylabel('Yaw sensor (deg)')
legend('lap 3','lap 2','lap 1')
plot (x1,y1)
hold on
plot (x2,y2,'r')
hold on
plot (x3,y3,'m')
xlabel('Distance (m)')
ylabel('Speed (km/h)')
legend ('lap 1','lap 2','lap 1')
plot (x1,y1)
hold on
plot (x2,y2,'r:')
hold on
plot (x3,y3,'m--')
legend ('20 mph','10 mph','5 mph')

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xlabel('Distance (m)')
ylabel('Vertical acceleration (G)')
plot3(x,y,z,'b’)
hold on
xlabel ('title (unit)')
ylabel ('title (unit)')
zlabel ('title (unit)')
title ('title')
legend (‘title’)