4,712 research outputs found

    Utilization of Lidar Technology — When to Use It and Why

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    Lidar technologies can assist transportation agencies during the design, construction, and maintenance phases of transportation projects. While Lidar has numerous applications, successfully deploying Lidar technologies is only possible if agencies have a solid understanding of their use cases and potential limitations. This report offers guidance the Kentucky Transportation Cabinet (KYTC) can use when making decisions on how to employ Lidar technologies in highway contexts. In addition to reviewing Lidar platforms and comparing Lidar-driven surveying to traditional surveying methods, the report examines challenges related to processing and storing Lidar data and the safety benefits Lidar technologies confer in the field. Brief case studies are presented which describe how Kentucky Transportation Center researchers have used Lidar to improve KYTC operations and asset management. Examples include measuring structure clearances, analyzing problematic road geometrics and monitoring slides. While Lidar technologies are beneficial, they are not appropriate for every project. As project size and complexity increases, the benefits of using Lidar technologies multiply

    Deep Generative Modeling of LiDAR Data

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    Building models capable of generating structured output is a key challenge for AI and robotics. While generative models have been explored on many types of data, little work has been done on synthesizing lidar scans, which play a key role in robot mapping and localization. In this work, we show that one can adapt deep generative models for this task by unravelling lidar scans into a 2D point map. Our approach can generate high quality samples, while simultaneously learning a meaningful latent representation of the data. We demonstrate significant improvements against state-of-the-art point cloud generation methods. Furthermore, we propose a novel data representation that augments the 2D signal with absolute positional information. We show that this helps robustness to noisy and imputed input; the learned model can recover the underlying lidar scan from seemingly uninformative dataComment: Presented at IROS 201

    Measurement and Evaluation of Roadway Geometry for Safety Analyses and Pavement Material Volume Estimation for Resurfacing and Rehabilitation Using Mobile LiDAR and Imagery-based Point Clouds

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    Roadway safety is a multifaceted issue affected by several variables including geometric design features of the roadway, weather conditions, sight distance issues, user behavior, and pavement surface condition. In recent years, transportation agencies have demonstrated a growing interest in utilizing Light Detecting and Ranging (LiDAR) and other remote sensing technologies to enhance data collection productivity, safety, and facilitate the development of strategies to maintain and improve existing roadway infrastructure. Studies have shown that three-dimensional (3D) point clouds acquired using mobile LiDAR systems are highly accurate, dense, and have numerous applications in transportation. Point cloud data applications include extraction of roadway geometry features, asset management, as-built documentation, and maintenance operations. Another source of highly accurate 3D data in the form of point clouds is close-range aerial photogrammetry using unmanned aerial vehicle (UAV) systems. One of the main advantages of these systems over conventional surveying methods is the ability to obtain accurate continuous data in a timely manner. Traditional surveying techniques allow for the collection of road surface data only at specified intervals. Point clouds from LiDAR and imagery-based data can be imported into modeling and design software to create a virtual representation of constructed roadways using 3D models. From a roadway safety assessment standpoint, mobile LiDAR scanning (MLS) systems and UAV close-range photogrammetry (UAV-CRP) can be used as effective methods to produce accurate digital representations of existing roadways for various safety evaluations. This research used LiDAR data collected by five vendors and UAV imagery data collected by the research team to achieve the following objectives: a) evaluate the accuracy of point clouds from MLS and UAV imagery data for collection roadway cross slopes for system-wide cross slope verification; b) evaluate the accuracy of as-built geometry features extracted from MLS and UAV imagery-based point clouds for estimating design speeds on horizontal and vertical curves of existing roadways; c) Determine whether MLS and UAV imagery-based point clouds can be used to produce accurate road surface models for material volume estimation purposes. Ground truth data collected using manual field survey measurements were used to validate the results of this research. Cross slope measurements were extracted from ten randomly selected stations along a 4-lane roadway. This resulted in a total of 42 cross slope measurements per data set including measurements from left turn lanes. The roadway is an urban parkway classified as an urban principal arterial located in Anderson, South Carolina. A comparison of measurements from point clouds and measurements from field survey data using t-test statical analysis showed that deviations between field survey data and MLS and UAV imagery-based point clouds were within the acceptable range of ±0.2% specified by SHRP2 and the South Carolina Department of Transportation (SCDOT). A surface-to-surface method was used to compute and compare material volumes between terrain models from MLS and UAV imagery-based point clouds and a terrain model from field survey data. The field survey data consisted of 424 points collected manually at sixty-nine 100-ft stations over the 1.3-mile study area. The average difference in height for all MLS data was less than 1 inch except for one of the vendors which appeared to be due to a systematic error. The average height difference for the UAV imagery-based data was approximately 1.02 inches. The relatively small errors indicated that these data sets can be used to obtain reliable material volume estimates. Lastly, MLS and UAV imagery-based point clouds were used to obtain horizontal curve radii and superelevation data to estimate design speeds on horizontal curves. Results from paired t-test statistical analyses using a 95% confidence level showed that geometry data extracted from point clouds can be used to obtain realistic estimates of design speeds on horizontal curves. Similarly, road grade and sight distance were obtained from point clouds for design speed estimation on crest and sag vertical curves. A similar approach using a paired t-test statistical analysis at a 95% confidence level showed that point clouds can be used to obtain reliable design speed information on crest and sag vertical curves. The proposed approach offers advantages over extracting information from design drawings which may provide an inaccurate representation of the as-built roadway

    Synergizing Roadway Infrastructure Investment with Digital Infrastructure for Infrastructure-Based Connected Vehicle Applications: Review of Current Status and Future Directions

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    The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The safety, mobility, environmental and economic benefits of Connected and Autonomous Vehicles (CAVs) are potentially dramatic. However, realization of these benefits largely hinges on the timely upgrading of the existing transportation system. CAVs must be enabled to send and receive data to and from other vehicles and drivers (V2V communication) and to and from infrastructure (V2I communication). Further, infrastructure and the transportation agencies that manage it must be able to collect, process, distribute and archive these data quickly, reliably, and securely. This paper focuses on current digital roadway infrastructure initiatives and highlights the importance of including digital infrastructure investment alongside more traditional infrastructure investment to keep up with the auto industry's push towards this real time communication and data processing capability. Agencies responsible for transportation infrastructure construction and management must collaborate, establishing national and international platforms to guide the planning, deployment and management of digital infrastructure in their jurisdictions. This will help create standardized interoperable national and international systems so that CAV technology is not deployed in a haphazard and uncoordinated manner
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