详情

Efficient Extraction and Evaluation of Complex Pavement Markings from Mobile Laser Scan Data

项目名称：	Efficient Extraction and Evaluation of Complex Pavement Markings from Mobile Laser Scan Data
摘要：	Recently, in their research with the Oregon Department of Transportation (ODOT), the research team developed an automated method for extracting linear lane markings from mobile laser scan (MLS) data as well as evaluating the retroreflectivity of those markings. In the current Pactrans project the team is 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, the team projects the MLS data into 2D to generate an intensity image and segment high intensity pixels, likely representing various road markings. 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.
状态：	Active
资金：	60000
资助组织：	Office of the Assistant Secretary for Research and Technology
管理组织：	Oregon State University, Corvallis
项目负责人：	Olsen, Michael J
执行机构：	Oregon State University, Corvallis
主要研究人员：	Olsen, Michael J
开始时间：	20180816
预计完成日期：	20200815
实际结束时间：	0