Methods for Improving Point Cloud Authenticity in LiDAR Simulation for Autonomous Driving: A Review
Collecting LiDAR data for autonomous driving using real vehicles is costly, scenario-limited, and challenging to annotate. Simulated LiDAR point clouds offer flexible configurations, reduced costs, and readily available labels but often lack the realism of real-world data. This study provides a comp...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10824761/ |
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| _version_ | 1841550876491120640 |
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| author | Yanzhao Yang Jian Wang Xinyu Guo Xinyu Yang Wei Qin |
| author_facet | Yanzhao Yang Jian Wang Xinyu Guo Xinyu Yang Wei Qin |
| author_sort | Yanzhao Yang |
| collection | DOAJ |
| description | Collecting LiDAR data for autonomous driving using real vehicles is costly, scenario-limited, and challenging to annotate. Simulated LiDAR point clouds offer flexible configurations, reduced costs, and readily available labels but often lack the realism of real-world data. This study provides a comprehensive review of methods to enhance the authenticity of simulated LiDAR data, focusing on simulation scenarios, environmental conditions, and point cloud features. Additionally, we discuss verification techniques, including direct and indirect methods, to assess authenticity improvements. Experimental results demonstrate the effectiveness of these techniques in enhancing perception algorithm performance. The paper identifies challenges in simulating LiDAR data, such as accuracy discrepancies, brand adaptability, and the need for comprehensive evaluation metrics. It also proposes future directions to bridge the gap between simulated and real-world data, aiming to optimize hybrid training models for improved autonomous driving applications. |
| format | Article |
| id | doaj-art-d0804403ef164e8cbfa188206696fb54 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-d0804403ef164e8cbfa188206696fb542025-01-10T00:00:58ZengIEEEIEEE Access2169-35362025-01-01134562458010.1109/ACCESS.2025.352580510824761Methods for Improving Point Cloud Authenticity in LiDAR Simulation for Autonomous Driving: A ReviewYanzhao Yang0https://orcid.org/0009-0002-4833-4256Jian Wang1https://orcid.org/0000-0002-7701-8511Xinyu Guo2https://orcid.org/0000-0001-5100-355XXinyu Yang3https://orcid.org/0000-0002-2285-3851Wei Qin4https://orcid.org/0009-0001-5821-3776College of Computer Science and Technology, Jilin University, Changchun, ChinaCollege of Computer Science and Technology, Jilin University, Changchun, ChinaChina Automotive Innovation Corporation, Nanking, ChinaChina Automotive Innovation Corporation, Nanking, ChinaChina Automotive Innovation Corporation, Nanking, ChinaCollecting LiDAR data for autonomous driving using real vehicles is costly, scenario-limited, and challenging to annotate. Simulated LiDAR point clouds offer flexible configurations, reduced costs, and readily available labels but often lack the realism of real-world data. This study provides a comprehensive review of methods to enhance the authenticity of simulated LiDAR data, focusing on simulation scenarios, environmental conditions, and point cloud features. Additionally, we discuss verification techniques, including direct and indirect methods, to assess authenticity improvements. Experimental results demonstrate the effectiveness of these techniques in enhancing perception algorithm performance. The paper identifies challenges in simulating LiDAR data, such as accuracy discrepancies, brand adaptability, and the need for comprehensive evaluation metrics. It also proposes future directions to bridge the gap between simulated and real-world data, aiming to optimize hybrid training models for improved autonomous driving applications.https://ieeexplore.ieee.org/document/10824761/LiDAR simulationpoint cloud simulationpoint cloud authenticitysimulation verificationautonomous driving |
| spellingShingle | Yanzhao Yang Jian Wang Xinyu Guo Xinyu Yang Wei Qin Methods for Improving Point Cloud Authenticity in LiDAR Simulation for Autonomous Driving: A Review IEEE Access LiDAR simulation point cloud simulation point cloud authenticity simulation verification autonomous driving |
| title | Methods for Improving Point Cloud Authenticity in LiDAR Simulation for Autonomous Driving: A Review |
| title_full | Methods for Improving Point Cloud Authenticity in LiDAR Simulation for Autonomous Driving: A Review |
| title_fullStr | Methods for Improving Point Cloud Authenticity in LiDAR Simulation for Autonomous Driving: A Review |
| title_full_unstemmed | Methods for Improving Point Cloud Authenticity in LiDAR Simulation for Autonomous Driving: A Review |
| title_short | Methods for Improving Point Cloud Authenticity in LiDAR Simulation for Autonomous Driving: A Review |
| title_sort | methods for improving point cloud authenticity in lidar simulation for autonomous driving a review |
| topic | LiDAR simulation point cloud simulation point cloud authenticity simulation verification autonomous driving |
| url | https://ieeexplore.ieee.org/document/10824761/ |
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