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作者(中文):石桂華
作者(外文):Shi, Gui-hua
論文名稱(中文):用於聯網自動駕駛汽車的動態光達點雲錯誤隱藏
論文名稱(外文):Error Concealment of Dynamic LiDAR Point Clouds for Connected and Autonomous Vehicles
指導教授(中文):徐正炘
指導教授(外文):Hsu, Cheng-Hsin
口試委員(中文):胡敏君
黃俊穎
口試委員(外文):Hu, Min-Chun
Huang, Chun-Ying
學位類別:碩士
校院名稱:國立清華大學
系所名稱:資訊系統與應用研究所
學號:110065466
出版年(民國):112
畢業學年度:111
語文別:英文
論文頁數:64
中文關鍵詞:光達動態點雲點雲錯誤隱藏自動駕駛汽車協同感知
外文關鍵詞:Error ConcealmentLiDAR Point CloudDriving AutomationCooperative Perception
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聯網自動駕駛汽車 (CAV) 通常配備各種感測器(RGB相機、光達、毫米波雷達等),以提升駕駛安全性。儘管動態光達點雲可以透過無線網路從 CAV 流式傳輸至邊緣伺服器以執行復雜運算的分類任務,但文獻中尚未討論由不可靠的無線網路引起的點雲幀不完整的問題。在本文中,我們首創了一個光達的錯誤隱藏 (LEC) 算法,以修復由於丟失或延遲封包導致的不完整點雲幀,以最小化點雲幀與原始點雲幀之間的倒角距離。利用機器學習技術,我們的 LEC 算法透過比較相鄰點雲幀的不完整比率自動地執行時間預測 (TP)、空間插值 (SI) 或時間插值 (TI) 等策略來修復不完整的點雲幀。我們使用由我們實作的協同模擬器以及 KITTI 里程計資料集來評估我們的 LEC 算法的效能。我們的模擬結果表明,所提出的 LEC 算法在倒角和豪斯多夫距離方面優於 TP、SI 和 TI 算法高達 82.68% 和 30.17%,並且在 C-V2X 網路中執行時間始終介於360--570 ms。此外,我們的 LEC 算法在 DSRC 網路中的倒角和豪斯多夫距離方面也優於其他算法高達 87.43% 和 66.58%。
Connected and Autonomous Vehicles (CAVs) often come with sensors, such as LiDARs, to improve road safety. Although dynamic LiDAR point clouds could be streamed over wireless networks from CAVs to edge servers for computationally-intensive classification tasks, the problem of incomplete point cloud frames caused by unreliable wireless networks has yet to be investigated in the literature. In this thesis, we propose the very first LiDAR Error Concealment (LEC) algorithm, to conceal incomplete point cloud frames due to lost or late packets for minimizing the Chamfer distance between the concealed and original point cloud frames. Driven by machine learning techniques, our LEC algorithm adaptively performs Temporal Prediction (TP), Spatial Interpolation (SI), or Temporal Interpolation (TI) to conceal incomplete point cloud frames by comparing the incomplete ratios of adjacent point cloud frames. We evaluate the performance of our LEC algorithms with a comprehensive co-simulator built upon the popular CARLA and NS-3 and the KITTI Odometry dataset. Our simulation results reveal that the proposed LEC algorithm outperforms the TP, SI, and TI algorithms by up to 82.68% and 30.17% in Chamfer and Hausdorff distances, and terminates in 360-570 ms in a C-V2X network. Moreover, our LEC algorithm also outperforms other algorithms by up to 87.43% and 66.58% in Chamfer and Hausdorff distances in a DSRC network.
1 Introduction 1
1.1 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.3 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2 Background 6
2.1 Connected and Autonomous Vehicles (CAVs) . . . . . . . . . . . . . . . 6
2.2 Driving Automation Datasets . . . . . . . . . . . . . . . . . . . . . . . . 7
2.3 Machine Learning for Scene Understanding . . . . . . . . . . . . . . . . 7
2.4 Error Concealment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
3 Related Work 10
3.1 Cooperative Perception . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.2 Point Cloud Caching . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.3 Point Cloud Completion . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.4 Point Cloud Interpolation . . . . . . . . . . . . . . . . . . . . . . . . . . 11
4 LiDAR Point Clouds Streaming 13
4.1 Properties of LiDAR Point Clouds . . . . . . . . . . . . . . . . . . . . . 13
4.2 Measurements of LiDARs . . . . . . . . . . . . . . . . . . . . . . . . . 14
4.3 System Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
4.4 Network Protocols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
4.5 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
5 Error Concealment of LiDAR Point Clouds 19
5.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
5.2 Temporal Prediction: TP . . . . . . . . . . . . . . . . . . . . . . . . . . 19
5.3 Spatial Interpolation: SI . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
5.4 Temporal Interpolation: TI . . . . . . . . . . . . . . . . . . . . . . . . . 21
5.5 Threshold-based LiDAR Error Concealment Algorithm: TLEC . . . . . . 21
5.6 LiDAR Error Concealment Algorithm: LEC . . . . . . . . . . . . . . . . 23
6 Evaluations 25
6.1 Implementations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
6.2 Experiment Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
6.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
7 Conclusion and Future Work 54
7.1 Concluding Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
7.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
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