Efficient Extraction and Evaluation of Complex Pavement Markings from Mobile Laser Scan Data
PI: Michael Olsen (OSU), olsen@oregonstate.edu, ORCID: 0000-0002-2989-5309
Co PIs: none
AMOUNT & MATCH: $30,000 from PacTrans; $30,000 Match
PERFORMANCE PERIOD: 8/16/2018 – 8/15/2020
STATUS: Completed
CATEGORIES: Pavement Markings, Laser Scanning
DESCRIPTION: Recently, in our research with the Oregon Department of Transportation (ODOT), the research team developed an automated method for extracting linear lane markings from MLS data (Figure-a) as well as evaluating the retroreflectivity of those markings. In the current Pactrans project we are building upon that effort to develop advanced techniques to handle more complex markings (e.g., pedestrian crosswalks, chevrons, and arrows) that were not considered in the prior project, but important to support mobility for multi-modal transportation. First, we project the MLS data into 2D to generate an intensity image and segment high intensity pixels, likely representing various road markings (Figure-b). Subsequently, a deep learning neural network approach, which is known for its high performance for object recognition in many applications, is used to classify various types of markings. This research will enable performance-based procedures for transportation agencies to evaluate pavement marking quality by providing detailed information, including retroreflectivity and types of markings, ranging from high resolution data on a single stripe to aggregated data and analyses statewide. This, in turn, supports informed decision making by DOT management for effective resource allocation. Improved maintenance of pavement markings will also lead to improved mobility with technologies such as autonomous vehicles.
DELIVERABLE | DUE DATE | DATE RECEIVED |
Research Project Progress Report #1 | 4/10/2019 | 4/4/2019 |
Research Project Progress Report #2 | 10/10/2019 | 10/15/2019 |
Research Project Progress Report #3 | 4/10/2020 | 4/1/2020 |
No Cost Extension Request | 6/15/2020 | N/A |
Draft Report | 6/15/2020 | 6/12/2020 |
Final Project Report | 8/15/2020 | 10/12/2020 |