Active Research
Below you will find a selection of active research projects related to the use of 3D capture in transportation applications. If you know of any relevant ongoing research projects or would like us to post one of your own projects, please us know through our CONTACT US page.
Advanced, 3D Infrastructure Information Modeling Using Lidar
Institution(s): Oregon State University
PI: Olsen, M.J.
Research Staff: Che, E., Jung, J., Kashani, A., Mahmoudabadi, H., Shaefer, K., O’Banion, M.S.
Funding Agency: National Science Foundation, 1351487
Web Link: http://goo.gl/cZ9Hbs
ABSTRACT: The primary research focus of this Faculty Early Career Development (CAREER) Program award is to efficiently identify and extract meaningful information from three-dimensional, geospatial models of transportation infrastructure in a holistic, automated framework, enabling broader application.
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Advanced mapping technologies such as laser scanning produce three-dimensional maps, creating highly detailed scenes that can be virtually explored and queried for a diverse range of purposes including infrastructure management, digital terrain modeling, cultural heritage, flood plain delineation, and landslide detection. However, tradeoffs exist between the detail and scale provided by these technologies and the immense size of the resulting datasets. This complexity can strain the most powerful computational resources and require a steep learning curve to exploit the data. While recent tools have made significant progress, only a small and piece-meal portion of information can be automatically extracted from these rich datasets compared to what is actually available. Key scientific questions to be addressed through this research include (1) What inherent attributes of an object and associated representation in laser scan data and supporting imagery are most beneficial to accurately identifying and extracting an object?, (2) How can neighboring features and context of an object help with rapidly identifying it within geospatial data?, and (3) How can an abridged framework be developed to improve information extraction from laser scan data to consider the broad range of transportation objects? This overarching framework will consider a broad range of object types, incorporate advanced system information and data structuring, function in noisy, real-world environments, and focus on datasets covering large spatial scales consistent with transportation infrastructure management. Products resulting from this framework include a transportation infrastructure object properties database, fully-classified benchmark datasets, new algorithms, and supporting code, which will be made publicly available.
Well-maintained transportation infrastructure is vital to our economy as well as public safety. Most transportation agencies charged with maintaining infrastructure are trying to develop a comprehensive methodology for inventory, maintenance and management of their immense assets. In many cases, the available resources are reduced while maintenance demands still increase. This research will provide timely solutions to map and digitally manage these assets more efficiently and cost-effectively than current practices. Although primarily focused on transportation, the computational methods and techniques will be applicable and extendable to a wide range of other applications such as land management, urban mapping, and robotics. This project also will provide students with multi-disciplinary education and training in geospatial analysis, computer science, transportation, and engineering. Despite the high demand for geospatial expertise today, educational opportunities are limited and challenging because of the rapid evolution of the supporting technologies. As a result, the U.S. has an insufficient number of geospatially-trained students entering the workforce to meet the ever-increasing demand utilizing geospatial information throughout society. This project will enhance geospatial education through activities ranging from exposure at public events to training camps for high school age students to creation of a model civil engineering geomatics graduate program.
3D Virtual Sight Distance Analysis Using Mobile LIDAR Data
Institution(s): Oregon State University
PI: Olsen, M.J.
Co-Investigators: Hurwitz, D., Kashani, A.
Funding Agency: Pacific NW Transportation Consortium (PACTRANS)
Web Link: http://goo.gl/1pSZYy
ABSTRACT: This research explores the feasibility, benefits and challenges of a safety analysis for sight distances based on DOT Mobile Laser Scanning (MLS) data. This research will also develop a systematic MLS data analysis framework to evaluate sight distances in different practical scenarios. The use of high density MLS data for sight distance analysis provides a data driven solution to aid decision making for safe transportation, directly aligning with the PacTrans FY2014-2015 theme. Further, it fits directly within Topic Area #3 Technological Impacts on Safety.
Unmanned Aircraft System Assessments of Landslide Safety for Transportation Corridors
Institution(s): University of Alaska Fairbanks, Oregon State University, University of Washington
PI: Cunningham, K.
Co-Investigators: Olsen, M.J., Wartman, J.
Funding Agency: Pacific NW Transportation Consortium (PACTRANS)
Web Link: http://goo.gl/Arwtyh
ABSTRACT: The proposed research addresses Pacific Northwest Transportation Consortium (PACTRANS) research priority of using new data-driven technologies to improve the safety of transportation systems in the Northwest United States. Landslides pose significant threats to the safety of motorists throughout the mountainous terrain of the Pacific Northwest The research will advance landslide safety assessment for transportation corridors by capitalizing on recent advances in unmanned aircraft systems (UAS) and new low-cost Structure from Motion (SfM) photogrammetry techniques. The resulting improved hazard assessment techniques will facilitate cost-effective evaluation of landslide safety across the broadly distributed transportation networks of the Pacific Northwest.
A Platform for Proactive Risk-based Slope Asset Management Phase II
Institution(s): University of Alaska Fairbanks, Oregon State University, University of Washington
PI: Cunningham, K.
Co-Investigators: Olsen, M.J., Wartman, J.
Funding Agency: Pacific NW Transportation Consortium (PACTRANS)
Web Link: http://goo.gl/gPas3q
ABSTRACT: Unstable slopes, including coherent landslides, rock falls, and debris flows, present significant risk to safety and regional commerce. This risk is a long-term concern that highway managers contend with on an on-going basis. The widespread spatial and temporal distribution of these landslides poses a number of challenges when deciding when, where, and how to allocate funds for mitigation efforts to maintain these assets. This challenge is compounded by the high level of effort currently required to survey, inspect and sample slopes for the purpose of condition assessment as part of an asset management program. Slope assessment has traditionally been costly and laborious, limiting it to a few sites.
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However, routine assessment is altogether necessary due to the potential consequences of a failure. Current best-practices for management do not necessarily facilitate proactive slope management – identifying and remediating hazardous conditions before a failure occurs. Current inventory systems are time consuming to complete (years) and generally only provide basic information after a collapse has occurred and likely caused damage. As such, they do not provide an understanding of how risk varies with time and location. However, a proactive, near-automated approach for the identification of possibly unstable locations prior to catastrophic failure offers the potential to significantly enhance public safety and reduce overall operation and repair costs. Advanced technologies such as Mobile Laser scanning (MLS) show great promise in quickly and frequently assessing large sections of highway. Time-series datasets from the MLS system enable remote assessment of slope stability with a higher level of confidence than current probabilistic studies based on inventories due to a significantly higher spatial and temporal resolution achievable with MLS.The scope of the current PACTRANS-funded project Phase I, entitled “A Platform for Proactive Risk-based Slope Assessment” includes the development of qualitative relative risk model for slope stability assessment using terrain models created from MLS data. In the second phase of the work, we will focus on quantitative time-series analysis using MLS data and integrating this information into the model developed in the first phase of research and into an agency’s transportation asset/performance management program.
A Synthesis Study on Collecting, Managing, and Sharing Road Construction Asset Data
Institution(s): Purdue University
PI: Cai, H., Dunston, P.
Funding Agency: Joint Transportation Research Program (JTRP), SPR-3707
Web Link: https://engineering.purdue.edu/JTRP/projects
ABSTRACT: The construction phase is the best time for collecting as-built data for roadside assets. Underground drainage and utilities are only accessible during construction. Collecting data for guardrails, crash cushions, signal and signage, and pavement after construction has not kept pace with technology. This project assesses INDOT’s data needs and recommends best practices in collecting, managing, and sharing road construction asset data. Main deliveries include (1) a framework of data needs, data collection opportunities during construction, and data collection and management strategies; and (2) an implementation guideline that specifies the best timing, method, and responsible party for data collection.
Laser Mobile Mapping Standards and Applications in Transportation
Institution(s): Purdue University
PI: Johnson, S., Bethel, J.
Funding Agency: Joint Transportation Research Program (JTRP), SPR-3706
Web Link: https://engineering.purdue.edu/JTRP/projects
ABSTRACT: This project will update the INDOT Survey Design Manual to incorporate standards for Lidar Mobile Mapping data acquisition. We will construct a test facility and evaluate commercial system accuracy. We will recommend workflows to obtain vector data from raw point clouds, and compare to conventional data collection approaches.
Segmenting, Grouping and Tracking Vehicles in LIDAR Data
Institution(s): NEXTRANS, Purdue University, West Lafayette
PI: Coifman, B.
Funding Agency: Research and Innovative Technology Administration, 01537819
Web Link: https://rip.trb.org/view/2013/P/1324050
ABSTRACT: Roadway congestion impacts almost all aspects of everyday lives in the United States (US), from safety, to the environment, to the quality of life, to the cost of goods and services. A comprehensive understanding of the traffic conditions over space that give rise to congestion remains elusive. To date, these issues have been studied predominantly with macroscopic data from point detectors (e.g., loop detectors), aggregated over fixed time periods ranging from 20 sec to 15 min. Many new theories have emerged in recent years to explain several on-going anomalies in traditional traffic flow theory. At the core of these new theories is the presence of non-trivial disturbances that last far less than the fixed time aggregation periods commonly used to study traffic, and thus, these micro-disturbances have not been empirically observed.
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If these theories are proven empirically, they should lead to better congestion management and control. The proposed research seeks to develop the tools to measure traffic flow at a resolution sufficiently precise to measure the micro-disturbances and prove or disprove the traffic flow theories that depend on their presence. Under support from National Science Foundation (NSF) and Federal Transit Administration (FTA), OSU has developed an instrumented probe vehicle that includes positioning sensors (differential global positioning system [DGPS] and inertial navigation) and ranging sensors (six LIDAR, one radar). The focus of the RNS is the one forward facing and one rear facing LIDAR sensors. These LIDAR collect a rich, 180° scan out to 80 m, in a plane approximately 0.5 m above the roadway, at 40 Hz. Although hundreds of hours of data have been collected, the tools to automatically reduce this vast quantity of data to useful information still need to be developed. The proposed research would undertake the task of segmenting the vehicle returns from the non-vehicle objects in the LIDAR data, grouping the vehicle returns into discrete vehicles, and tracking the resulting vehicle groups across scans. Once these tools are developed, they would be used to mine hundreds of hours of existing instrumented probe vehicle data.
Guaranteed LiDAR-aided Multi-object Tracking at Road Intersections
Institution(s): NEXTRANS, Purdue University, West Lafayette
PI: Tarko, A.P.
Funding Agency: Research and Innovative Technology Administration, 01566588
Web Link: https://rip.trb.org/view/2015/P/1357862
ABSTRACT: A Traffic Scanner (TScan) is being developed to enable collecting accurate microscopic traffic data at road intersections with an innovative use of Light Detection and Ranging (LiDAR) three-dimensional (3D) laser scanning technology. LiDAR sensing promises to overcome certain limitations of video cameras because it yields 3D point clouds that have a one-one correspondence with the environment being sensed. The current effort is focused on developing elements of the LiDAR’s tracking algorithm with self-calibration and adjustment for the sensor’s motion.
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The results of the current project show that LiDAR calibration and tracking with clear statistical guarantees are possible. The guarantees are functions of the characteristics of the sensor itself: its resolution, and precision. The project expects that the sensing system will work in a variety of environments and will produce results of a uniform quality. The second phase will be focused on developing algorithms for object identification, classification, and tracking.