隨著科技日益滲透至日常生活，大型城市正面臨日益嚴峻的交通流動性挑戰。自動駕駛汽車產業被視為解決這類問題的一個有前景的領域，但目前車輛相關技術尚還未達到理想中的完全自動化水平，而實現完全自動化的關鍵在於自動駕駛汽車感知技術的發展，因此感知技術的企業於產業中的發展將使社會逐步邁向一個更加智能、高效和安全的交通未來，而企業為制定相應的發戰策略，其必須理解市場中各家企業專利技術能力、專利角色定位與未來發展趨勢，透過以上指標制定適切的發展策略。
本研究主要的目的是提供自動駕駛汽車感知技術相關企業未來發展建議。首先，本研究利用社會網路分析針對 MTrends 專利資料庫中的自動駕駛汽車感知技術專利進行中心性分析，基於社會網路分析計算各家企業的中心性數值分析結果，提出評估企業專利技術能力的三個關鍵指標包含「專利技術影響力」、「專利技術市場模仿能力」和「獨立技術開發能力」，應用企業專利技術能力指標定位企業的專利技術角色；接著，本研究透過類神經網絡分析各企業過往的專利公開件數，建立專利技術生命週期並分析其專利技術的發展趨勢，從而發現該專利的技術生命週期仍表現出持續成長的趨勢
最後，根據本研究的專利技術角色定位與專利技術生命週期為成長趨勢的分析結果，為企業和技術開發人員提供企業發展策略建議，使企業和投資者更準確地理解企業當前狀況，並深化對產業結構和競爭動態的認識，從而促進更有效的未來規劃。

As technology increasingly permeates daily life, large cities are facing more severe challenges in traffic mobility. The autonomous driving car industry is seen as a promising field to address these issues. However, current vehicle-related technologies have not yet achieved the ideal level of complete automation. The key to achieving full automation lies in the development of autonomous driving car perception technologies. Thus, the advancement of perception technology enterprises in the industry will gradually lead society towards a more intelligent, efficient, and safer transportation future. To formulate appropriate strategic plans, enterprises must understand the patent technology capabilities, patent role positioning, and future development trends of various companies in the market, and develop suitable strategies based on these indicators.
The main purpose of this study is to provide suggestions for the future development of enterprises related to autonomous driving car perception technologies. Firstly, this study uses social network analysis to perform centrality analysis on patents of autonomous driving car perception technologies in the MTrends patent database. Based on the centrality values calculated through social network analysis, the study proposes three key indicators for evaluating corporate patent technology capabilities, which include ' influence of patented technology', 'patent technology market imitation ability', and 'independent technology development capability'. These indicators are used to position the patent technology roles of enterprises. Next, the study analyzes the number of patent disclosures of each company over time using neural network analysis, establishing the patent technology lifecycle and analyzing the development trends of its patent technology. This reveals that the lifecycle of this patent technology still shows a continuing growth trend.
Finally, based on the analysis results of patent technology role positioning and the growth trend of the patent technology life cycle from this study, suggestions for corporate development strategies are provided for businesses and technology developers. These suggestions help enterprises and investors to more accurately understand the current status of their businesses and deepen their understanding of industry structures and competitive dynamics, thereby promoting more effective future planning.

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