在現在的無人車駕駛技術中，行人偵測是相當重要的議題。為了防止交通碰
撞事故，無論白天或夜晚，行人都應該精準地被偵測。由於視覺影像在夜晚拍攝
時不夠清晰，本論文提出在不需要視覺影像的狀況下，利用超清晰度的LIDAR 進
行行人偵測的方法。為了處理LIDAR 點雲在長距離時點雲數量不夠的問題，本文
提出一種新的解決方法來提高正確率，透過距離感知擴展的方式將三維點雲映射
到二維平面，並提取出相對應的二維輪廓及其相關的二維特徵。除了人工設計的
特徵外，本文也考慮到深度特徵，基於多項特徵結合分類，本方法在F1 測量法中
取得比現有技術高出23% 的良好結果。

Pedestrian detection is one of the key technologies of driver assistance system. In order to prevent potential collisions, pedestrians should be always accurately identified whether during the day or at night. Since the visual images of the night are not clear, this thesis proposes a method for recognizing pedestrians by using a high-definition LIDAR without visual images. In order to handle the long-distance sparse point problem, a novel solution is introduced to improve the performance. The proposed method maps the three-dimensional point cloud to the two-dimensional plane by a distance-aware expansion approach and the corresponding 2D contour and its associated 2D features are then extracted. In addition to hand-crafted features, deep learned features are also considered in this thesis. Based on multiple cues, the proposed method obtains significant performance boosts over state-of-the-art approaches by 23% in terms of F1-measure.

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