在進行專利分析時，經常會使用引用網路分析，而引用分析當中之主路徑分析更是經常被使用。主路徑分析方法主要由兩個部分構成，一為權重計算，二為主路徑搜尋方法。在主路徑搜尋方法的選擇上，關鍵連結（key-route）延伸主路徑搜尋方法是常被選擇的。
在進行專利分析前，研究人員通常對於一技術領域的重要專利文獻有一定認識，但不論是何種主路徑搜尋方法，皆無法讓此一資訊與主路徑分析相結合，實屬可惜。因此，本研究使用一不同於現有之主路徑搜尋方法，提供研究人員選擇「關鍵節點」（Key-node）-亦即重要的專利文獻-做為主路徑搜尋之起始。對於該技術領域不具備既有知識之研究人員亦可使用量化方法（例如被引用數等）來決定關鍵節點。
本研究以自動駕駛車輛領域為例，利用pivotalness數值量化方法決定關鍵節點，並使用關鍵節點主路徑搜尋方法搜尋主路徑。在探討關鍵節點主路徑之使用場景上，本研究除了單獨使用關鍵節點主路徑搜尋方法外，本研究還將其與傳統上較常被選擇之關鍵路徑主路徑搜尋方法進行搭配，進而搜尋出一更為完整且具有主要與次要路徑標示之自駕車發展軌跡。
在研究結果方面，當單獨使用關鍵節點主路徑搜尋方法時，本研究於初步取得主路徑之後，再就其引用網路之特性於原先之主路徑基礎上進行調整以符合要求。拜關鍵節點主路徑搜尋方法之特性所賜，研究人員能夠便利地就原先主路徑不足之處進行補償，提供研究人員較大之研究彈性。
當關鍵節點主路徑與關鍵路徑主路徑綜合時，則可以得到一更為完整且具有主要與次要路徑標示之主路徑。於自動駕駛車輛之研究領域中可以發現，由關鍵節點主路徑所「補充」之路徑，能夠有效地協助解讀發展軌跡。

Main path analysis is a bibliometric method capable of tracing the most significant paths in a citation network and is commonly used to trace the development trajectory of a research field.
Regardless of the main path search methods like global, local or key-route, they do not have the capability to let the researchers to utilize their existing knowledge of the research domain on performing main path research.
To conquer this disadvantage, this study proposes a different approach: key-node main path search method. Key-node main path search method capable of searching the main path from one or more key-nodes assigned by researchers base on their existing knowledge of the research domain. In addition, for the researchers that don’t have the existing knowledge, they can decide the key-nodes by performing some bibliometric methods. This study decides the key-nodes by ranking the so-called pivotalness value of nodes.
This study applies the key-node main path analysis on autonomous vehicles domain. Besides using key-node main path search method, this study compares key-node main path and key-route main path to get a more comprehensive view of a field.
In terms of research results, this study proposes 2 conditions of using key-node main path search method. In the first condition, this study set a goal that all the selected important nodes should be contained in the main path. After the initial main path is obtained, this study continuously adjusts the main path to achieve the goal. Due to the feature of key-node main path search method, the researcher can easily adjust the main path by choosing different key-node.
In second condition, this study integrate the key-node main path and the key-route main path, a complete main path with important and minor path can be obtained. In the research field of autonomous vehicles, it can be found that the path supplemented or enhanced by the key-node main path search method can help the researchers interpret the development path effectively.

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