近幾十年來，計算力、人工智慧、感測器技術和機器學習的進步顯著加速了自動駕駛系統的發展。如今，自動駕駛車輛正在世界各地的公共道路上進行測試，例如Waymo和Cruise等公司正將這些車輛部署於公共運輸用途。期望自動駕駛能夠在最少人為干預下，在複雜城市環境中達到定位、路線規劃和決策等功能。在安全性的考量下，強大且高精度的導航對於自動駕駛車輛的發展至關重要尤其是在城市環境下。本論文旨在利用車用級感測器以實現在城市環境地區的車道內等級導航（三維均方根誤差小於0.5公尺）。
在透空良好的條件下，傳統上車輛導航依賴全球衛星定位系統。然而GNSS信號易受環境干擾，特別是多路徑和非直視路徑信號，這些訊號干擾會導致定位上的系統性誤差。在這樣的干擾環境中，導航的準確性和穩健性無法滿足自動駕駛車輛的需求。
為了解決GNSS信號干擾的問題，多感測器融合已成為開發穩健自駕車導航系統的解決方案。目前，自動駕駛車輛主要使用機械式光達，因為它具有良好的精度和掃描範圍。由於其體積和重量，對於未來自動駕駛車輛的大量生產來說是不切實際的。本論文提出使用固態光達結合已知高精點雲地圖並利用正態分佈轉換演算法進行車輛姿態和位置估計。固態光達與機械式光達相比，其缺點為掃描範圍角度小，在沒有良好初始位置和姿態下進行點雲匹配時，匹配結果會落入區域最佳估計而非全域最佳估計，造成匹配錯誤的情形發生。
為了克服上述掃描範圍小的問題，本論文提出藉由慣性導航與全球衛星定位系統整合和固態光達感測器相互補的特性，提出一個穩健且低成本的導航製圖單元架構和演算法。其中，慣性導航系統與全球導航衛星系統整合可以為地圖匹配提供良好初始姿態和位置估計。慣性導航系統與全球導航衛星系統整合又可以區分為鬆耦合與緊耦合兩種架構。鬆耦合架構配合實時動態技術可以提供公分級定位精度，但在都市環境下多路徑效應產生的系統誤差無法消除，造成鬆耦合架構過度信賴衛星定位系統的估計。鬆耦合在進行觀測量更新時，由於其架構設計需要至少四個衛星，對於都市挑戰的環境下是相對困難的。相比之下，緊耦合設計無需至少四個衛星就可以向濾波器提供虛擬距離和都普勒觀測量的更新，此作法可以提高在都市挑戰環境下導航的穩定性和軌跡的平滑性。然而為了達到與實時動態技術輔助鬆耦合相當之定位精度，緊耦合設計需要解決虛擬距離系統誤差修正的問題。
為了修正系統誤差，傳統上需要利用差分技術來消除，但差分技術需要足夠多的基準站，基礎建設的成本高，透過高精地圖的測繪，除了提供路面資訊外，也提供了良好的位置和幾何資訊。本論文提出一個次級卡爾曼濾波器，利用固態光達與高精地圖匹配來實時估計每顆衛星的虛擬距離誤差。通過結合緊耦合的慣性導航系統/全球導航衛星系統（INS/GNSS）與固態光達（SSL）和高精地圖匹配的優勢，本論文提出了一成本效益高的固態光達輔助緊耦合INS/GNSS演算法架構，用於城市環境中的自動駕駛車輛導航。本論文提出的系統在嚴苛的城市場景下進行驗證皆可以實現三維30公分內的定位精度，即車道內等級導航要求之精度。

Over the decades, advancements in computing power, artificial intelligence, sensor technology, and machine learning have significantly accelerated the development of autonomous driving systems. Today, autonomous vehicles are being tested on public roads around the world, with companies like Waymo and Cruise leading the way in deploying these vehicles for public and commercial use. The current state of autonomous driving encompasses vehicles capable of navigating complex urban environments with minimal human intervention, relying on sophisticated technologies such as localization, route planning and decision making. Considering safety concerns, robust and high-accuracy navigation in challenging urban environments is vital for the development of autonomous vehicles. This thesis aims to enhance the implementation of automotive-grade sensors for in-lane level navigation (<0.5 m in 3D) under urban challenging areas.
Traditionally, vehicle navigation and positioning systems have relied on the Global Navigation Satellite System (GNSS), especially in open-sky conditions. However, GNSS signals are susceptible to disruptions caused by environmental interferences, notably multipath and non-line-of-sight (NLOS) signal receptions, which introduce systematic errors in position estimation. In such environments, the navigation accuracy and robustness may not meet the requirements of autonomous vehicles.
To resolve the challenge of GNSS signal interference, multi-sensor fusion has emerged as a solution in developing robust navigation systems for autonomous vehicles. At present, the commercial autonomous vehicles mainly use mechanical Light Detection and Ranging (LiDAR) since it has ideal precision. However, it is impractical for its size and weight for mass production of autonomous vehicles in the future. In this thesis, the use of Solid-State LiDAR (SSL) with prior map and the normal distribution transformation (NDT) algorithm for vehicle pose estimation is proposed. Nevertheless, SSL's limited field of view (FoV), when compared to mechanical LiDAR, presents a notable limitation, potentially resulting in NDT matching failures.
To overcome this, this thesis proposes to leverage the complementary of the INS/GNSS integration system with SSL. The INS/GNSS integration system could provide the initial guess for map matching. The INS/GNSS system, which can be divide into loosely and tightly coupled design. The loosely coupled design, aided by GNSS methods like Real-Time Kinematic (RTK), offers centimeter-level accuracy but struggles with systematic errors in urban scenarios and requires at least four satellites. In contrast, the tightly coupled design could provide pseudorange and doppler measurement update to the navigation filter, enhancing navigation solution stability and trajectory smoothness. This design, however, necessitates addressing pseudorange error correction.
This thesis utilizes a secondary Kalman filter to estimate real-time per-satellite pseudorange error using NDT matching positions. By combining the strengths of tightly coupled INS/GNSS and SSL map matching, this thesis proposes a cost-effective SSL-aided tightly coupled INS/GNSS solution for autonomous vehicle navigation in urban challenging environments. The proposed system is evaluated under harsh urban scenarios and could achieve localization accuracy within 30 cm by utilizing the SSL NDT-aided pseudorange error correction filter.

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