近年隨著車用導航的需求增加，提供精準與穩健的導航估計技術已成為關鍵。為了滿足先進輔助駕駛系統與高度自動駕駛的應用，其導航系統的絕對定位精度需優於1.5公尺（車道級）才能滿足其車輛控制及輔助導引的各項要求。本論文著重在發展與研究基於車用級感測器的整合架構，提升其導航效能俾滿足車道級的絕對定位需求。
目前以全球衛星定位系統為核心的導航系統，受限於多路徑效應與非直視性訊號等錯誤觀測量干擾，其定位效能在都市地區受到極大的挑戰。一般而言，該技術整合慣性導航系統進行效能改善是測繪製圖等專業領域的經典手段；然而其仰賴高階感測器作為精準定位的解決方案，無法切合民用車端導航市場所需。傳統低成本車用級衛星接收機、慣性導航系統、輪速計與氣壓計的整合方案，於衛星錯誤訊號環伺的艱困環境中均難以有效滿足其定位精度需求。近期於高度自動駕駛的導航解決方案中，基於光學類型感測器，使用同步定位與地圖建構技術與高精地圖點雲資料，透過匹配與地圖融合方法來達成精準定位，在雛型發展的過程中蔚為主流。惟此類方案的困難點在於：（1）光學感測器如光達、相機不具成本效益；（2）高精地圖點雲資料過於龐大，難以廣泛應用；（3）運算量大，即時導航需配備高階運算電腦。不利於普及一般民用車輛。
有鑑於此，本論文針對車道級定位精度需求，發展基於車用級感測器之無縫導航解決方案，除對目前一般車用導航方案進行全面性的分析，並提出多項整合策略與方法如下：（1）結合載波相位跨時差分觀測量，設計具位置域平滑化效果之衛星定位解算濾波器(GNSS filter)，並結合穩健回歸方法(robust regression)進行觀測量適應性調權；（2）設計準緊耦合式慣導系統與衛星定位系統整合架構，提出基於慣導輔助之週波脫落偵測機制(INS-aided cycle slip detection)、錯誤訊號緩解方法、基於慣性測量單元零偏誤差與位置狀態誤差即時監測之錯誤偵錯與排除機制；（3）建構基於向量高精地圖輔助機制，提出側向位置向量更新模型、週波未定值解算結果驗證方法、衛星觀測量錯誤偵錯與排除機制。此外，本研究同時針對道路級應用精度(< 5 m)，提出衛星定位/輪速計整合方案，發展基於輪速輔助之觀測模型與錯誤偵錯與排除機制。
本論文採用導航等級或高階戰術級的慣性導航系統輸出軌跡作為參考真值，並於衛星反射訊號充斥之城市峽谷地區進行驗證。實驗結果顯示，使用車用級感測器，基於準緊耦合之慣導系統/衛星定位/輪速計整合架構，搭配向量高精地圖輔助與本研究提出各項方法，在都市環境下於三維方向可以滿足車道級絕對定位精度。同時，基於衛星定位/輪速計之整合方案於單點定位模式下亦能滿足道路級定位精度。本研究整合策略與方法具運算及成本效益，且穩定性高，能實際布署至一般車用設備，可望在各項民生導航用途上有顯著貢獻。

With the increasing demands for seamless land-vehicle navigation, systems with robust performance are required. For advanced driver assistance systems (ADAS) and highly automated driving (HAD) applications, studies are recently conducted to achieve high accuracy and robustness of on-vehicle guidance. This dissertation aims to enhance the implementation of automotive-grade sensors for absolutely lane-level navigation (< 1.5 m) in 3-D.
In current navigation systems based on a Global Navigation Satellite System (GNSS), the faulty signals from multipath and non-line-of-sight (NLOS) reception are significant challenges in urban areas. Conventionally, the fusion of GNSS and inertial navigation system (INS) is widely expected to improve the performance and bridge GNSS outages. The integration based high-end INS and GNSS components may provide an accurate solution but is not the market-favorable design. On the other hand, its low-cost combination with the aid of common automotive sensors such as odometer and barometer struggles to improve the performance during multipath/NLOS contamination, especially for the vertical accuracy in the long-term. The current solution aiming to HAD intends to use simultaneous and localization (SLAM) algorithms with optical sensors and a high-definition map (HD map) with point cloud to fulfill the accuracy requirement. However, there are challenges: (1) optical sensors, such as Light Detection And Ranging (LiDAR) and camera, are not cost-effective; (2) an HD point-cloud map has huge data size, difficult to implement in the current vehicle; (3) high-end computing power is significant for the real-time navigation.
In the far majority of prior research, techniques and integrated systems proposed to solve or mitigate problems are for HAD prototypes. The capability to obtain a lane-level navigation solution in 3-D with a sub-meter level in the vertical for general vehicles using automotive-grade sensors is beneficial for promoting ADAS and HAD applications. This dissertation evaluates the currently common solutions and proposes a lane-level accuracy solution using the following designs: (1) time-differenced carrier phase (TDCP) embedded GNSS filter with robust regression for position-domain smoothing and adaptive reweighting; based on these approaches, an alternative scheme without INS, GNSS/Odometer integration with odometer-based measurement model and FDE, is additionally investigated; (2) INS/GNSS semi-tightly-coupled (semi-TC) integration scheme with false-GNSS mitigation, by INS-aided cycle slip detection, faulty-signals mitigation, and fault detection and exclusion (FDE) based on on-line monitoring of IMU bias error state and position error state; (3) HD vector map aiding with the proposed measurement model, validation for ambiguity resolution (AR), replacing the GNSS float solution in the vertical with the HD vector map, and measurement-domain FDE.
These proposed designs are evaluated and based on a large number of trajectories collected by different sensors in various scenarios. In general, the 3-D performance of the proposed designs, based on INS/GNSS/Odometer semi-TC integration scheme with HD vector map aiding, can satisfy the lane-level accuracy requirement. The vertical accuracy can reach to sub-meter level, which benefits not only the identification of complex scenarios in the vertical for human driving (ADAS) but electric vehicles due to power management and computer decision (HAD). The proposed designs constitute an effective computing solution and collective compensation for the individual drawbacks of the integrated system, with great potential for current and future navigation applications, including ADAS and HAD.

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