Abstract
State departments of transportation often maintain extensive "video logs"of their roadways that include signs, lane markings, as well as non-image-based information such as grade, curvature, etc. In this work we use the Roadway Information Database (RID), developed for the Second Strategic Highway Research Program, as a surrogate for a video log to design and test algorithms to detect rumble strips in the roadway images. Rumble strips are grooved patterns at the lane extremities designed to produce an audible queue to drivers who are in danger of lane departure. The RID contains 6,203,576 images of roads in six locations across the United States with extensive ground truth information and measurements, but the rumble strip measurements (length and spacing) were not recorded. We use an image correction process along with automated feature extraction and convolutional neural networks to detect rumble strip locations and measure their length and pitch. Based on independent measurements, we estimate our true positive rate to be 93% and false positive rate to be 10% with errors in length and spacing on the order of 0.09 meters RMS and 0.04 meters RMS. Our results illustrate the feasibility of this approach to add value to video logs after initial capture as well as identify potential methods for autonomous navigation.
| Original language | English |
|---|---|
| Article number | 050 |
| Journal | IS and T International Symposium on Electronic Imaging Science and Technology |
| Volume | 2020 |
| Issue number | 6 |
| DOIs | |
| State | Published - Jan 26 2020 |
| Event | 2020 Intelligent Robotics and Industrial Applications Using Computer Vision Conference, IRIACV 2020 - Burlingame, United States Duration: Jan 26 2020 → Jan 30 2020 |
Funding
We would like to acknowledge the support of Omar Smadi and The Iowa State Center for Transportation Research and Education. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). Work was the Federal Highway Administration of the US of Transportation, Exploratory Advanced Research funded by Department Fund. We would like to acknowledge the support of Omar Smadi and The Iowa State Center for Transportation Research and Education. This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan). Work was funded by the Federal Highway Administration of the US Department of Transportation, Exploratory Advanced Research Fund.
| Funders | Funder number |
|---|---|
| DOE Public Access Plan | |
| Department Fund | |
| Exploratory Advanced Research Fund | |
| Iowa State Center for Transportation Research and Education | DE-AC05-00OR22725 |
| U.S. Department of Energy | |
| U.S. Department of Transportation | |
| Federal Highway Administration |
Keywords
- Autonomy
- Computer vision
- Deep learning
- Segmentation
- Transportation systems