Bounded-Error LiDAR Compression for Bandwidth-Efficient Cloud-Edge In-Vehicle Data Transmission
Tallennettuna:
| Julkaisussa: | Electronics vol. 14, no. 5 (2025), p. 908 |
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| Päätekijä: | |
| Muut tekijät: | , , |
| Julkaistu: |
MDPI AG
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| Aiheet: | |
| Linkit: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3176377926 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2079-9292 | ||
| 024 | 7 | |a 10.3390/electronics14050908 |2 doi | |
| 035 | |a 3176377926 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231458 |2 nlm | ||
| 100 | 1 | |a Ray-I, Chang | |
| 245 | 1 | |a Bounded-Error LiDAR Compression for Bandwidth-Efficient Cloud-Edge In-Vehicle Data Transmission | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Recent advances in autonomous driving have led to an increased use of LiDAR (Light Detection and Ranging) sensors for high-frequency 3D perceptions, resulting in massive data volumes that challenge in-vehicle networks, storage systems, and cloud-edge communications. To address this issue, we propose a bounded-error LiDAR compression framework that enforces a user-defined maximum coordinate deviation (e.g., 2 cm) in the real-world space. Our method combines multiple compression strategies in both axis-wise metric Axis or Euclidean metric L2 (namely, Error-Bounded Huffman Coding (EB-HC), Error-Bounded 3D Compression (EB-3D), and the extended Error-Bounded Huffman Coding with 3D Integration (EB-HC-3D)) with a lossless Huffman coding baseline. By quantizing and grouping point coordinates based on a strict threshold (either axis-wise or Euclidean), our method significantly reduces data size while preserving the geometric fidelity. Experiments on the KITTI dataset demonstrate that, under a 2 cm bounded-error, our single-bin compression reduces the data to 25–35% of their original size, while multi-bin processing can further compress the data to 15–25% of their original volume. An analysis of compression ratios, error metrics, and encoding/decoding speeds shows that our method achieves a substantial data reduction while keeping reconstruction errors within the specified limit. Moreover, runtime profiling indicates that our method is well-suited for deployment on in-vehicle edge devices, thereby enabling scalable cloud-edge cooperation. | |
| 653 | |a Huffman codes | ||
| 653 | |a Storage systems | ||
| 653 | |a Communication | ||
| 653 | |a Bandwidths | ||
| 653 | |a Sensors | ||
| 653 | |a Encoding-Decoding | ||
| 653 | |a Data transmission | ||
| 653 | |a Error analysis | ||
| 653 | |a Lidar | ||
| 653 | |a Compression ratio | ||
| 653 | |a Geometry | ||
| 653 | |a In vehicle | ||
| 653 | |a Entropy | ||
| 653 | |a Data compression | ||
| 653 | |a Vehicles | ||
| 700 | 1 | |a Ting-Wei, Hsu | |
| 700 | 1 | |a Yang, Chih | |
| 700 | 1 | |a Yen-Ting, Chen | |
| 773 | 0 | |t Electronics |g vol. 14, no. 5 (2025), p. 908 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3176377926/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3176377926/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3176377926/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |