隨著科技的不斷進步，自動駕駛技術已成為一種發展趨勢。在自動駕駛系統中，路徑規劃和路徑追蹤是極為關鍵的技術。在發展初期，自動駕駛車輛的應用場景主要集中在二維平面道路上，並未將高度、俯仰角以及翻滾角納入考量。然而，當自駕車應用於越野地形時，地形的崎嶇程度對路線規劃的可行性以及路徑追蹤的舒適性和安全性有著顯著影響。
有鑑於此，本研究提出了一種適用於越野地形的路徑規劃方法，這一方法融合了全球衛星導航系統（GNSS）、慣性量測單元（IMU），並對傳統的Hybrid A*演算法進行了改進。本研究使用GNSS和IMU收集的數據來建立一個三維網格地圖，通過改進的演算法考慮車輛的俯仰角和翻滾角作為成本因素進行權重計算，以優化車輛的通行能力和路徑距離。
本方法特別適用於地勢崎嶇的路段。通過評估車輛的姿態資訊，本研究助於在複雜地形中找出一條既舒適又安全的路徑，從而提升乘車體驗。這種路徑規劃方法不僅提高了自動駕駛系統在複雜地形中的適應性，也為乘客提供了更安全、平穩的駕駛環境。此外，本研究的方法在實際應用中顯示出良好的性能，對於提高自駕車在崎嶇地形中的運行效率和安全性具有重要意義。
綜上所述，本研究的創新之處在於將先進的導航技術與改進的演算法結合，為越野地形下的自動駕駛車輛提供了一種有效的路徑規劃解決方案。這不僅展示了自動駕駛技術在多元化應用環境中的潛力，也為未來在更加複雜環境中的自動駕駛系統的發展奠定了基礎。

As technology continues to advance, the development of autonomous driving technology has become a trend. Path planning and path tracking are crucial aspects of autonomous vehicle (AV) technology. Initially, the application of AVs was primarily focused on two-dimensional road surfaces, neglecting considerations such as elevation, pitch, and roll angles. However, when applied to off-road terrain, the ruggedness of the terrain significantly impacts the feasibility of route planning and the comfort and safety of path tracking.
Recognizing this, this study introduces a path planning method suitable for off-road terrain, integrating Global Navigation Satellite System (GNSS), Inertial Measurement Unit (IMU), and an improved Hybrid A* algorithm. This study constructs a three-dimensional grid map using data from GNSS and IMU, optimizing vehicle traversability and distance by weighting the vehicle's pitch and roll angles as cost factors in the algorithm.
The proposed method is particularly applicable to rugged terrains. By evaluating vehicle posture information, it helps identify the most comfortable and safe routes through complex terrains, thereby enhancing the riding experience.This research's innovation lies in the combination of advanced navigation technologies with an improved algorithm, providing an effective path planning solution for AVs in off-road terrain. It not only demonstrates the potential of autonomous driving technology in diverse application environments but also lays the foundation for the future development of AV systems in more complex contexts. The method has shown promising performance in practical applications, significantly improving the efficiency and safety of AVs navigating rough terrains.
In summary, this study presents a novel approach to path planning for AVs in off-road conditions, focusing on enhancing stability and safety while navigating challenging landscapes.

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