高精地圖（High Definition Map, HD_Map）是輔助自動駕駛車輛（Autonomous Vehicles, AV）所需的高精度三維地圖，完整包含道路上或道路附近與行車有關的空間資訊要素，有助於自駕車的定位、引導、導航及避險等。應用移動式測繪系統（Mobile Mapping System, MMS）獲取資料自動化測製高精地圖是趨勢，本研究嘗試從MMS光達點雲自動萃取並分類道路標記，所採用的理論是一種轉移式的深度學習類神經網路，簡稱為轉移學習（Transfer Learning）。其資料處理流程包括前處理、訓練、萃取分類、及精度評估，前處理作業是先過濾非路面點雲再將點雲轉換為網格式的強度值影像。訓練過程是從選取的訓練資料進行手動註釋和拆分，建立訓練和測試數據集，訓練數據集也可以採用既有的公開資料庫，再利用現有訓練資料擴充之。之後即運用訓練好的機器學習模型從光達強度影像中萃取分類路面標記，然後以人工判讀的成果為參考評估測試成果精度，先評估萃取的正確度、錯誤率、及F1指標，進而評估分類的誤差率。最後也進一步將分類的點雲向量化。實驗結果顯示，最好的測試模型是以5cm解析度的光達強度影像來預訓練U-Net模型。基於F1指標低於都低於15% 的評估，驗證所提方法可成功萃取並分類道路標記，其測試成效與最新發表的論文成果相當。然而，所提方法之萃取完整度優於參考文獻方法，但分類精度則不如所比較的方法，主要原因是本研究同時進行萃取及分類，而比較的方法則先萃取，進而濾除雜訊點群後再進行分類。因此建議未來研究建議可嘗試將萃取和分類過程分開，在兩者之間增加一個濾除機制，進一步降低錯分類誤率。

High Definition (HD) Maps are highly accurate 3D maps that contain features on or nearby the road that assist with navigation in autonomous vehicles (AVs). One of the main challenges when making such maps is the automatic extraction and classification of road markings. In this paper, a methodology is proposed to use transfer learning to extract and classify road markings from mobile LiDAR. Initially, point clouds were filtered and converted to intensity-based images using several grid cell sizes. Then, it was manually annotated and split to create the training and testing datasets. The training dataset has undergone augmentation before serving as input for evaluating openly available multiple pre-trained neural network models. The models were then applied to the testing dataset and assessed based on their precision, recall, and F1 scores for extraction as well as their error rates for classification. Further processing generated classified point clouds and polygonal vector shapefiles. The results indicate that the best model is the pre-trained U-Net model trained from the intensity-based images with a 5cm resolution. It was able to achieve F1 scores that are comparable with recent work and error rates that are below 15%. However, the classification results are still two or three times greater than those of recent work and as such, it is recommended to separate the extraction and classification procedures, having a step in between to remove misclassifications.

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