臺灣博碩士論文加值系統

本論文之研究為使用低價位市售導航機、慣性感測器與電子地圖開發一套分別以資料融合技術和交互式多模型為基礎的動態估測定位演算法，目的在於將定位準確度提升至一個車道內。定位提升演算法主要利用無損式卡爾曼濾波器進行感測器資料融合與估測器設計，透過無損轉換的過程將可能出現的取樣點散佈於估測中心點附近，並利用車輛動態感知器進行運動模型的機率指標計算，偵測出當下車輛運動行為是否為直行、變換車道或爬坡，最後結合電子地圖資訊回饋給估測器進行取樣點的邊界條件限制，藉此提升定位效果。
本研究演算法以車輛模擬軟體(CarSim)取得道路與車輛資料進行不同車速與動態下定位效果的驗證，並與一般估測定位演算法進行比較後結果顯示本研究之演算法定位效果明顯優於一般估測定位，演算法能額外得知更多道路與車輛動態資訊提供於駕駛者使用；而本系統也分別在台大與水源快速道路進行實車道路定位驗證，其定位效果皆能夠穩定提升定位準確度至一個車道內。

This research proposes an approach that is able to locate vehicle position with lane level precision using low-cost multi-sensor fusion including commercial GNSS, IMU and digital maps. The approach is based on interactive multiple models (IMM), data fusion, and unscented Kalman filter techniques. The unscented Kalman filter (UKF) technique is used to design the estimator of the vehicle position, as well as executing data fusion which integrates multiple sensor data. The sigma points around the position center will be calculated by unscented transform, representing the probability of vehicle position. In this research, the probability of vehicle motion is also estimated by the motion sensor through IMM, including longitudinal motion, lateral motion and slope motion. For the estimation result, digital maps will be used to increase the precision of the vehicle position by providing road information and attributes. By utilizing the constraints such as road boundary on UKF, the sigma points positions can be realigned according to the position reference, increasing the precision of vehicle position.
The algorithms proposed in this research uses road and vehicle information obtained from vehicle dynamics simulation software CarSim to validate positioning precision with different vehicle velocity and motion. The results when compared with general cases demonstrated significant enhancement on vehicle positioning, with the proposed algorithm able to gather more road and vehicle motion related data for the driver. Finally, the proposed system has been validated using experimental vehicle driven around the NTU campus and Shue-Yuan expressway, with results showing consistent positioning precision elevation down to lane level.

誌謝 I
摘要 II
Abstract III
目錄 IV
圖目錄 VI
表目錄 XII
符號表 XIII
第一章 緒論 1
1.1 研究動機與目的 1
1.2 文獻回顧 2
1.3 研究貢獻 5
第二章 系統模型架構與實驗設備介紹 6
2.1 系統架構 6
2.2 衛星定位系統簡介 7
2.2.1 座標系統 7
2.2.2 全球導航衛星系統 9
2.2.3 星基擴增系統 11
2.2.4 虛擬基準站即時動態定位技術 12
2.3 商用車用設備與測試環境簡介 14
2.4 車輛動態模型簡介 16
2.5 高精準電子地圖簡介 21
2.5.1 量測儀器簡介 21
2.5.2 地圖屬性簡介 23
第三章 圖資輔助與資料融合技術 31
3.1 地圖匹配演算法 31
3.2 濾波器設計 35
3.2.1 卡爾曼濾波器 35
3.2.2 無損式卡爾曼濾波器 37
3.3 動態測模組設計 43
3.3.1 慣性導航與資料融合估測定位 43
3.3.2 交互式多模型技術 46
3.3.3 車輛動態預測結合定位模組技術 48
3.4 圖資輔助駕駛策略模組設計 58
3.4.1 交流道出入口偵測模組 58
3.4.2 變換車道警示/偵測模組 60
第四章 模擬結果分析討論 62
4.1 模擬架構與環境介紹 62
4.2 交互式多模型與一般慣性導航定位估測結果比較 64
4.2.1 二維動態估測定位模擬 65
4.2.2 三維動態估測定位模擬 85
4.3 限制條件下動態估測定位模擬 89
4.3.1 高速/快速道路情境下之限制條件動態估側 89
4.3.2 相同干擾與偏差下於不同速度之限制條件動態估側 95
第五章 實車驗證模擬結果 126
5.1 台大校內實車測試 126
5.2 水源快速道路實車測試 141
第六章 結論與未來建議 150
6.1 結論 150
6.2 未來建議 151
參考文獻 152

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