Abstract
Motivation
Maintenance departments need to regularly assess the quality of the roads in order to properly maintain them. Currently, this is done by yearly inspections or in response to reports from the general public. It would be advantageous to continuously monitor the road surface so that damages like rutting and potholes can be detected as soon as they occur. Furthermore, detection of precursor signs like cracks will allow the maintenance crews to address problem areas before they develop into serious problems. We want the system to be inexpensive and easy to run.
Description
Approach
Our approach is to collect images, GPS and other data with smartphones, use computer vision algorithms to analyze the images, and save the results in the database of the maintenance department where it can be displayed to the user or further analyzed. Additional sensors and devices like OBDII or structured light sensors could be added to get additional data.
The current prototype system that we are developing is shown in the photo below. A smartphone is mounted on the windshield and is powered through the cigarette lighter. A device is plugged into the OBDII and sends out readings via bluetooth. While the car is driving the smartphone collects images of the outside and tags them with GPS and all the other information. The data is transmitted to a central computer via Wifi where it is analyzed. The second photo shows how the data can be displayed in Google Earth. The user can zoom in to the location of interest in the city and click on a small icons which brings up an image of the location.
In this project we want to build a full system that can automatically collect and store data over long periods of time, obtains road distress classification that complies with current practices and makes the data available to the user. We have completed the first version of the collection system and the software to clean the data and display it to the user. We are at the very beginning of a pilot test in which the City of Pittsburgh uses the system, integrates it in their workflow and evaluates its effectiveness. We are close to a first version of the analysis software that can score the road distress.
Timeline
February 2012 - December 2013
Strategic Description / RD&T
Deployment Plan
-
Expected Outcomes/Impacts
-
Expected Outputs
TRID
Individuals Involved
| Email |
Name |
Affiliation |
Role |
Position |
| cmertz@andrew.cmu.edu |
Mertz, Christoph |
Carnegie Mellon University |
PI |
Faculty - Tenured |
Budget
Amount of UTC Funds Awarded
$217697.94
Total Project Budget (from all funding sources)
$191214.00
Documents
Match Sources
No match sources!
Partners
| Name |
Type |
| City of Pittsburgh |
Deployment Partner Deployment Partner |
