Virtual-real-fusion simulation framework for evaluating and optimizing small-spatial-scale placement of cooperative roadside sensing units
Roadside sensing units’ (RSUs) perception capability may be substantially impaired by occlusion issue even they work cooperatively. However, the joint influence of static and dynamic occlusions in real-life situations remains inadequately considered in optimizing RSUs’ placement. This study proposes...
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sg-ntu-dr.10356-1800062024-09-13T15:33:49Z Virtual-real-fusion simulation framework for evaluating and optimizing small-spatial-scale placement of cooperative roadside sensing units Ma, Yang Zheng, Yubing Wang, Shuyi Wong, Yiik Diew Easa, Said M. School of Civil and Environmental Engineering Engineering Sensing unit Simulation framework Roadside sensing units’ (RSUs) perception capability may be substantially impaired by occlusion issue even they work cooperatively. However, the joint influence of static and dynamic occlusions in real-life situations remains inadequately considered in optimizing RSUs’ placement. This study proposes a virtual-real-fusion simulation (VRFS) framework that combines traffic simulation and point clouds of real-world road environment to optimize RSUs’ deployment. Point clouds and triangular meshes are used to model static and dynamic obstacles, respectively. A structure-retained spherical projection method is developed to efficiently emulate RSUs’ data collection. Based on the developed VRFS, the probabilistic occupancy maps (POM) are created to represent traffic scenarios. The POM-based cross entropy (CE) is proposed as the surrogate metric for evaluating the detection performance of cooperative RSUs. The Bayesian optimizer is applied to optimize the RSUs’ placement parameters (decision variables) by minimizing CE. Test results show that it is viable to use the POM-based CE as a proxy for evaluating cooperative RSUs’ sensing performance. Considering the occlusion effect adds to the efficacy of POM-based CE as a surrogate metric. Compared with traffic volume, the adverse effect of the proportion of large vehicles on RSUs’ detection performance is more significant. There are no significant patterns regarding how the optimized RSU positions vary with traffic parameters. The comparisons with existing methods further verify the importance of considering both static and dynamic occlusions in optimizing RSUs’ placement. Besides, the proposed method can yield better optimization results more efficiently than existing approaches. Published version This work was supported in part by the Fundamental Research Funds for the Central Universities under Grant JZ2023HGTA0191 and Grant JZ2022HGTA0338, in part by Anhui Provincial Natural Science Foundation under Grant 2308085QE188, the Natural Sciences and Engineering Research Council of Canada under Grant Ryerson-2020-04667, and in part by the Guangdong Science and Technology Strategic Innovation Fund (the Guangdong–Hong Kong–Macau Joint Laboratory Program) under Grant 2020B1212030009. 2024-09-09T07:22:35Z 2024-09-09T07:22:35Z 2024 Journal Article Ma, Y., Zheng, Y., Wang, S., Wong, Y. D. & Easa, S. M. (2024). Virtual-real-fusion simulation framework for evaluating and optimizing small-spatial-scale placement of cooperative roadside sensing units. Computer-Aided Civil and Infrastructure Engineering, 39(5), 707-730. https://dx.doi.org/10.1111/mice.13167 1093-9687 https://hdl.handle.net/10356/180006 10.1111/mice.13167 2-s2.0-85184435078 5 39 707 730 en Computer-Aided Civil and Infrastructure Engineering © 2024 The Authors. Computer-Aided Civil and Infrastructure Engineering published by Wiley Periodicals LLC on behalf of Editor. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. application/pdf |
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Engineering Sensing unit Simulation framework Ma, Yang Zheng, Yubing Wang, Shuyi Wong, Yiik Diew Easa, Said M. Virtual-real-fusion simulation framework for evaluating and optimizing small-spatial-scale placement of cooperative roadside sensing units |
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Roadside sensing units’ (RSUs) perception capability may be substantially impaired by occlusion issue even they work cooperatively. However, the joint influence of static and dynamic occlusions in real-life situations remains inadequately considered in optimizing RSUs’ placement. This study proposes a virtual-real-fusion simulation (VRFS) framework that combines traffic simulation and point clouds of real-world road environment to optimize RSUs’ deployment. Point clouds and triangular meshes are used to model static and dynamic obstacles, respectively. A structure-retained spherical projection method is developed to efficiently emulate RSUs’ data collection. Based on the developed VRFS, the probabilistic occupancy maps (POM) are created to represent traffic scenarios. The POM-based cross entropy (CE) is proposed as the surrogate metric for evaluating the detection performance of cooperative RSUs. The Bayesian optimizer is applied to optimize the RSUs’ placement parameters (decision variables) by minimizing CE. Test results show that it is viable to use the POM-based CE as a proxy for evaluating cooperative RSUs’ sensing performance. Considering the occlusion effect adds to the efficacy of POM-based CE as a surrogate metric. Compared with traffic volume, the adverse effect of the proportion of large vehicles on RSUs’ detection performance is more significant. There are no significant patterns regarding how the optimized RSU positions vary with traffic parameters. The comparisons with existing methods further verify the importance of considering both static and dynamic occlusions in optimizing RSUs’ placement. Besides, the proposed method can yield better optimization results more efficiently than existing approaches. |
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School of Civil and Environmental Engineering |
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School of Civil and Environmental Engineering Ma, Yang Zheng, Yubing Wang, Shuyi Wong, Yiik Diew Easa, Said M. |
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Article |
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Ma, Yang Zheng, Yubing Wang, Shuyi Wong, Yiik Diew Easa, Said M. |
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Ma, Yang |
title |
Virtual-real-fusion simulation framework for evaluating and optimizing small-spatial-scale placement of cooperative roadside sensing units |
title_short |
Virtual-real-fusion simulation framework for evaluating and optimizing small-spatial-scale placement of cooperative roadside sensing units |
title_full |
Virtual-real-fusion simulation framework for evaluating and optimizing small-spatial-scale placement of cooperative roadside sensing units |
title_fullStr |
Virtual-real-fusion simulation framework for evaluating and optimizing small-spatial-scale placement of cooperative roadside sensing units |
title_full_unstemmed |
Virtual-real-fusion simulation framework for evaluating and optimizing small-spatial-scale placement of cooperative roadside sensing units |
title_sort |
virtual-real-fusion simulation framework for evaluating and optimizing small-spatial-scale placement of cooperative roadside sensing units |
publishDate |
2024 |
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https://hdl.handle.net/10356/180006 |
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1814047235546021888 |