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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/60035
標題: | 結合無損式卡爾曼濾波器及交互式多模型車輛動態偵測技術之自駕系統三維圖資融合即時精準定位 3D Digital Map Data Fusion Enabled Real-time Precision Positioning For the Self-driving System Using the Unscented Kalman Filter and Interactive Multiple Model Based Vehicle Motion Detection Techniques |
作者: | Po-Fu Wu 吳柏富 |
指導教授: | 李綱(Kang-Li) |
關鍵字: | 交互式多模型,資料融合技術,車輛精準定位, Interactive Multiple Model,Data Fusion,Real-time Precision Positioning, |
出版年 : | 2016 |
學位: | 碩士 |
摘要: | 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. |
URI: | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/60035 |
DOI: | 10.6342/NTU201700081 |
全文授權: | 有償授權 |
顯示於系所單位: | 機械工程學系 |
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