本論文旨在利用車輛模型與粒子濾波器追蹤演算法發展自主式駕駛應用。一般來說，車輛導航系統包括即時環境感知，車輛定位，車輛避撞，路徑規劃以及路徑追隨控制。為了實現及開發智能自走車功能，本研究整合慣性感測元件（IMU），GNSS接收機和增量式編碼器進行載具動態估測。在演算法設計過程中，地圖輔助的路徑規劃提供一個追蹤之參考路線。
為此，開發於LabVIEW平台之人機界面（UMI）即可進行即時觀察路徑追蹤情況。在車輛預測控制的方面，本文利用粒子濾波器演算法於規劃軌跡上作路徑預測。遞迴式粒子濾波器以取樣權重估測位置並且推估轉向角響應。另外，所有應用之感測器皆整合至一個嵌入式運算平台並進行即時接收以及效能評估。搭配感測器以及嵌入式電腦平台之輕型電動車於校園內進行實驗並且路徑預測與轉向角之性能也由此驗證。

The thesis describes the design, implementation, and test of an autonomous vehicle navigation system using vehicle model and particle filter tracking algorithm. Typically, a vehicle navigation system comprises of real-time environment perception, vehicle localization, collision avoidance, path planning, and path following control. In order to implement the features for intelligent autonomous vehicle, a sensor suite of integrated inertial measurement unit (IMU), GNSS receiver, and incremental encoder is developed for vehicle dynamic estimation. In the design, a map-aided path planning strategy is employed to generate a reference route. To this end, a UMI (User Machine Interface) programming on LabVIEW platform is developed to facilitate the observation of a goal-oriented path tracking performance.
In the aspect of vehicle estimation control, the system utilizes particle filter algorithm to ensure that the actual trajectory follows the planned path. The recursive particle filter is able to weight the cells and response the angle as well as estimated position information. All the sensors are integrated into an embedded computer platform to assess the autonomous driving capability. The test is conducted on campus by installing the sensor suite and embedded computer platform into an electric vehicle. The trajectory tracking capability is preliminarily verified.

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