Morris Smith Study of Sign Characteristics on I-20

January-May 2014 CEE 2699

Dr. Yichang Tsai

I. Introduction

The overall objective of this research project is to assess and improve the effectiveness of the usage of LiDAR point cloud technology to automatically detect and inventory traffic signs. This method of sign inventory, if proven successful, would be a considerable advance in sign management and technology. Traffic signs are a seemingly small yet vital component of a stable infrastructure. All of the different types of signs are meant to convey simple and understandable messages to drivers in order to keep them focused on the road. This helps to promote safety such that accidents may be prevented, especially on highways, where car accidents have a much higher fatality rate than other roads. One way for traffic controllers and engineers to study the efficiency of these signs is to take an inventory in which GPS coordinates and sign type are considered, and this is why automatic sign detection would go a long way towards maintaining sustainable infrastructure.

II. Data Extraction

Before testing the LiDAR point cloud on Interstate 20 in Georgia, a ground truth was needed to serve as the control in which to compare it with. This was created by manually inventorying signs using the Trimble Spatial Imaging Analyst interface. An approach was taken similar to how a licensed traffic engineer would take in terms of the types of signs to be inventoried. All signs within certain constraints were considered; those that were not were temporary signs, guardrail markers, electronic signs, and signs on the right-of-way on an entrance/exit ramp.

a. Productivity

The entire set from which data was extracted covers approximately 200 miles from Alabama to South Carolina, eastbound and westbound. There are 136,016 frames in total in the set, so 400 miles of data yields an average of 340 frames per mile. The total number of inventoried signs is 3,847, and it took approximately 20 hours to collect all of them. This yields a productivity of 3.21 signs per minute, and 113.35 frames per minute. It is helpful to consider the sign distribution along the highway; the highest concentration of traffic signs was found in urban areas and suburban areas as well as segments of the highway that were under construction during data collection. A large portion of the highway, however, went through rural areas and had sparsely distributed signs, including many sections that just consisted of mile markers alone.

b. Pros and Cons of Data Extraction

Establishing a ground truth dataset had both positive and negative impacts on the overall project. The main reason behind creating the ground truth was to provide a default control dataset in which to compare LiDAR results with. A helpful aspect of the set was that it had coordinates for every sign with which a researcher could compare with the corresponding sign that was detected through LiDAR analysis. This would be very advantageous to anyone who would want to take a strictly analytical approach to improving the parameters. Preliminary data extraction would also allow new users to the LiDAR software to accustom themselves to the interface.

On the other hand, the extraction of the ground truth data was very tedious and time -consuming. It is also important to note several hours of research were lost during this phase due to severe weather conditions; this pushed back the LiDAR analysis many weeks, and the primary objective in which the project was aimed for was rushed and could only be carried out in a much shorter period of time than what was desired.

c. Issues

Three issues in particular were encountered multiple times during data extraction. The first one was that overhead signs, mostly road name signs, were particularly difficult to accurately pinpoint using the two-click triangulation method. Secondly, large vehicles such as trucks occasionally blocked a sign that was on the left hand side of the camera van. Lastly, data points on an opposite bound appeared to be inaccurate. The last issue is essentially an observation and does not affect the overall project.

d. Recommendations

For future users of this method of data extraction, it is recommended to inventory signs based on predicting whether or not the LiDAR detection will pick it up. People who are selected for this kind of research should have an ample amount of intuition to deduce whether a sign is big and reflective enough to be detected. It is understood that this project is for test purposes and will not be used by any department of transportation to study sign placement.

III. LiDAR-Based Sign Detection

Only one clip (Alabama to Georgia, Eastbound) was used for testing the LiDAR detection. There are five particular parameters that were considered when evaluating the effectiveness. These are the sensitivity, minimum elevation, the minimum distance from sign to sign, the maximum lateral distance, and the minimum hit count. Those that were not considered were parameters that measured the height and width of the signs.

a. Method of parametric study

The method which was used to evaluate the effectiveness of the sign detection was a basic visual analysis. After running the LiDAR tool, sign detection markers were compared with ground truth markers with respect to their locations. The effectiveness of the parameter set was evaluated based on what signs matched the ground truth set, which ones did not, how many markers were placed at each sign, etc. The results would be compared for about one quarter of the clip, so that general assumptions could be made about the rest of the data.

b. Observations

The issue that was run into the most while testing the LiDAR parameters was that there were multiple detection markers at many large signs. The initial conclusion was that the tool was considering the larger signs as more than one and essentially counted them as multiple signs in extremely close proximity. In response, the minimum distance from sign to sign was decreased for other datasets. This decreased the number of markers on a decent number of signs. Afterwards, a realization came that the large number of markers was very likely a result of a high amount of reflectivity on the signs. After that realization, the minimum hit count was increased, and the results showed a significant improvement in bringing the amount of markers for each sign to one.

c. Recommendations

One parameter that deals specifically with reflectiveness that could be probed is the sensitivity. There could be much more analysis put into this parameter, and it is highly suggested for whomever may pick this project up to do such analysis. Also recommended is to include a LiDAR camera for the left-

hand side of the road and create more clips for that; there may be parameters that are completely different than that of those of the right that would yield optimal performance.