Group:
The group had a phone call with the professor discuss progress and expectation moving for to be met by the deadline (7/5)
The group added another test trial for the Roadway Monitoring System during the week again meant to test the functionality of our test system.
Below is an image of all the sensors attached to the speed limit sign.
Here we began to test some of the current outputs given while the system is running, and the solar power panel has direct sunlight exposure.
We are also monitoring the current coming from the solar power panel in order to replenish the charge on the battery.
This shows the current value we were receiving from the solar panel in the closed loop circuit.
This is another demonstration of us testing the sensors on the road during sunny conditions and low traffic.
Jeremy:
I constructed the following graphs based on the data collected from the test trials.
The graph below shows the temperature sensor reading in the test trials that we experienced rain. There are two lines being displayed, one showing the SHT30 sensor temperature, and the actual temperature calculated from the Accuweather Website for the site testing was done on. The values collected for the sensor remained within the ±0.5% error.
The graph below shows the temperature sensor reading in the test trials that the group did not experience rain. This graph is still analyzing the SHT30 sensor temperature, and the actual temperature calculated from the Accuweather Website for the site testing was done on. The calculated temperature in the units of degrees in Fahrenheit.
The following chart shows the reading of the 1733 anemometer sensor of the roadway monitoring system in test trials with no precipitation. The two lines displayed, one of the sensor wind speeds and one of the actual wind speeds. The wind speed remained within a ±1% error margin.
The graph below shows the anemometer sensor readings of the roadway monitoring system test trials with precipitation. The chart is meant to show the sensor when readings versus the gathered actual wind speeds at our location test site in terms of miles per hour (mph). the interpretation of chart can deduce that the anemometer detected higher wind speeds than the actual wind speed. It is concluded that the wind speed picked up gusty winds as well as the constant wind speed where the actual wind speed was calculated as an average.
For the next chart below, this represents the rain sensor readings with test trials involving precipitation. The line graph shows the sensor readings while engaged by precipitation. The bar graph represents the actual measured rainfall in the testing site location. The relationship represented in the chart below shows that the rainfall over the time spent at our location site either represented a slower rainfall (>250) or a heavy rainfall (<250).
Destany:
I constructed a graph of the data gathered from the test trials regarding the pixy2's ability to detect vehicles. As you can see below, the average of the vehicles detected vs the vehicles counted are not very consistent. The percent error for the vehicles detected is between 28-38% when number of vehicles hit double digits. When the vehicles detected are between 1-10 the accuracy is more consistent where the percent error tends to stay around 20%. This is caused by trouble with detecting multiple vehicles when they pass in a group vs singular. The camera is able to detect more vehicles when they pass one by one vs 2 or more at a time. Overall, for the project, the percent error doesn't affect the outcomes being incorrect too often. Since the value we are detecting is more of a range vs precise number, the given outcome was correct 10/11 times. Our threshold for number of vehicles is, less than 10 is low traffic and more than 10 is high traffic. This threshold was proven to be correct giving us a 9% error for the number of trials done. The percent error for this sensor can be improved with a better-quality camera that has clarity for longer distances.
The graph below shows the different detection outcomes of the test trials during rainy conditions. When the rain increases, the accuracy of vehicle detection deceases. As shown below, the first trial indicates that the camera detected 5 cars while I counted 6 during a light drizzle of rain. For the third trial, the camera detected 2 cars while I counted 5 during heavier rain conditions. This took the outcome accuracy from less than 20% error to more than a 60% error.
Below reflects the serial monitor outcome from the trial shown above. The testing occurred during a sunny part of the day. The default speed limit for the area was 30 mph. The detections that happened during this trial was, the temperature being 87 degrees F, it was daylight, and the traffic was low. The sensors for rain and wind did not meet the threshold needed in order for their outcome to change.
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