自駕車的感知系統必須融合數種感測資訊，確認車輛位置及車輛週邊情境狀態，故而系統定位能力是為自駕系統之核心技術，更是實現無人全自動自駕導航功能之重要關鍵技術。
近年來，4D毫米波雷達技術發展為自駕領域帶來許多新可能進步空間；鑒於量測數據密度之顯著提升，促成自駕車於高度複雜場域應用上顯著發揮能力，更因具備不受天候干擾性能，相較於主流感測元件如相機及光達，更能顯現毫米波雷達元件之優勢。
本論文利用毫米波雷達量測靜態物體時，徑向速度與目標方向之間的關係，採用隨機抽樣一致之演算法，將雷達點雲分割為靜態點雲，例如建築及地面，或是動態點雲，例如移動中之人或車輛，可同時取得雷達傳回之運動瞬時速度，最終經過車輛運動模型可估計車輛在三維空間之運動狀態。本論文將習知之二維車輛自運動估算推展至三維計算，能夠在錯綜複雜及高低起伏道路條件，例如高速公路環狀交流道，來協助自駕車進行定位。鑒於此算法無須強大運算力及無須事先訓練之模型，較容易於車輛電腦上實現，且能即時運作且具強健之穩健性，相當適合應用於注重安全性之自駕車定位技術上。

The perception system of autonomous vehicles integrates measured results from various sensors via computational algorithms to understand its own position and the surrounding environment. Positioning capability is considered one of the core technologies in the field of autonomous driving and served as a crucial key in establishment of unmanned autonomous navigation.
Recently, the emergence of 4D millimeter-wave radar has brought many new possibilities to the field of autonomous vehicles. The significant improvement in measurement density excels to effective function in highly complex scenarios of autonomous vehicles. Additionally, the resilience to weather and environmental interference proves its superiority over mainstream cameras and LiDAR technology.
This thesis adopts millimeter-wave radar’s fixed relationship between the speed and motion direction angle when measuring static objects. With the Random Sample Consensus (RANSAC) algorithm, the radar point cloud is segmented into static point cloud and dynamic point cloud while simultaneously obtaining the instantaneous speed of radar motion. Finally, the vehicle motion model is used to estimate the vehicle's motion in three-dimensional space.
The research efforts extend the conventional two-dimensional vehicle ego-motion estimation to three-dimensional space in assisting the autonomous vehicle localization on complex and undulating roads and highways. The algorithm is easy to implement as it does not require powerful computing resources or pre-training models. It’s real-time operation and robustness make it suitable for autonomous vehicle localization technology that prioritizes safety.

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