因應自動化駕駛時代來臨，車輛之導航定位成為一重要議題。傳統車輛導航技術依賴全球定位系統，此方式之精度與無線訊號傳輸的環境與訊號品質息息相關，於車輛行駛的情境中，如：市區、山林或隧道......等，皆會有較不穩定的定位解產生，加入各項感測元件適時予以輔助，為一有效提升精度之方法。然而感測元件中包括對外在感測之相機，雷達……等，亦或是量測載具本身動態之慣性感測元件。相同地，外在感測元件同樣會受環境而影響效能，因此慣性感測與全球定位系統整合較為常見。傳統整合方式為利用擴展式卡爾曼濾波器進行車輛狀態的量測估計與更新，但在對於模型假設與訊號分布條件有一定之限制，往往會有特定情形失準之情況產生。本論文主要對於現行不同整合方式之定位準確度與運算量做探討，並以車輛行車動態作為區分，分別對於特定情境之定位結果加以分析，以供整合方式選用之參考。

Autonomous vehicles are becoming popular in modern society, so navigation of autonomous vehicles is now an important issue. The traditional positioning system is satellite navigation. The accuracy of satellite navigation depends on the environment and the quality of the signal. Perceptual sensors such as camera, Lidar, and inertial sensors may also assist navigation. Similarly, information from the external environment is subject to uncertainties. Using an inertial sensor integrated with the Global Navigation Satellite System is a common approach to solving this problem. The general method used to integrate two systems is the extended Kalman filter. However, there are some assumptions and approximations in the process that may not satisfy a complex vehicle dynamic system. This thesis discusses the positioning accuracy in multi driving scenarios where different fusion algorithms are used to compare the positioning performance.

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