A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Point cloud-based optimization of roadside LiDAR placement at constructed highways
Abstract Current approaches for optimizing the placement of roadside LiDAR (RSL) at constructed highways work on handcrafted scenes which fail to precisely map real-world situations. This study proposes a computer-aided framework to address the issue. First, high-accuracy point cloud data are introduced to model the as-built highway infrastructures, based on which an unsupervised clustering approach is applied to segment the target monitoring area (TMA). Then, candidate RSL locations are generated in a semi-automated manner combining manual delineation and spline resampling. Next, new deterministic and a U-net-based LiDAR models are separately developed to virtually estimate candidate RSL's joint coverage. Finally, based on the proposed sensor models, a detection matrix is created to facilitate the application of binary integer programming that minimizes the number of RSL while ensuring complete coverage of TMA. The tests on point cloud data of the three different sites demonstrate the effectiveness of the proposed workflow.
Graphical abstract Display Omitted
Highlights Introduce point cloud data as road background for optimization of sensors' placement Propose physics-based and deep neural network-based virtual sensor models Create a detection matrix to simplify the optimization problem Test the feasibility of a neural network as a sensor model
Point cloud-based optimization of roadside LiDAR placement at constructed highways
Abstract Current approaches for optimizing the placement of roadside LiDAR (RSL) at constructed highways work on handcrafted scenes which fail to precisely map real-world situations. This study proposes a computer-aided framework to address the issue. First, high-accuracy point cloud data are introduced to model the as-built highway infrastructures, based on which an unsupervised clustering approach is applied to segment the target monitoring area (TMA). Then, candidate RSL locations are generated in a semi-automated manner combining manual delineation and spline resampling. Next, new deterministic and a U-net-based LiDAR models are separately developed to virtually estimate candidate RSL's joint coverage. Finally, based on the proposed sensor models, a detection matrix is created to facilitate the application of binary integer programming that minimizes the number of RSL while ensuring complete coverage of TMA. The tests on point cloud data of the three different sites demonstrate the effectiveness of the proposed workflow.
Graphical abstract Display Omitted
Highlights Introduce point cloud data as road background for optimization of sensors' placement Propose physics-based and deep neural network-based virtual sensor models Create a detection matrix to simplify the optimization problem Test the feasibility of a neural network as a sensor model
Point cloud-based optimization of roadside LiDAR placement at constructed highways
Ma, Yang (author) / Zheng, Yubing (author) / Wang, Shuyi (author) / Wong, Yiik Diew (author) / Easa, Said M. (author)
2022-10-14
Article (Journal)
Electronic Resource
English
Sensor placement , Optimization , Roadside LiDAR , Point cloud data , Deep learning , AV , autonomous vehicles , BIP , binary integer programming , FOV , field of view , FPFH , fast point feature histogram , KNN , k-nearest neighbors , LiDAR , light detection and ranging , NCS , natural cubic splines , RMSE , root-means square error , RSL , roadside LiDAR , RSUs , roadside units , TMA , target monitoring area , VLM , virtual LiDAR model , 2D , two-dimensional , 3D , three-dimensional
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