本文研發一種結合人因照明與智慧控制的創新策略，運用可變光型之LED智能路燈設計，搭配影像式輝度計，基於IoT sensor-driven之理念，藉由輝度計偵測路面照明狀態，透過AI影像判斷車輛與輝度條件，可自動調適燈具輸出兩種不同型態的配光曲線，分別是平均照度與照度均勻度優先的照度光學設計，適合於未下雨的乾燥路面之視覺判別；及平均輝度與輝度均勻度優先的輝度光學設計，更適合於濕滑路面之行車狀況，有效提升駕駛人的視覺清晰度，可以解決雨後路面濕滑所造成的駕駛人無法正確辨識路面狀況及反射眩光之危害議題，優化全時段道路安全與用路人視覺清晰度，有助於改善長期以來，夜間下雨溼滑時所造成的交通意外。
另外透過燈具照射於改質瀝青、再生瀝青等新舊不同材質的瀝青道路鋪面之量測數據，評估路面的照明光學效果，可以估測鋪面輝度反射係數q，並與現場道路鋪面量測的國際糙度指標(International Roughness Index, IRI)、鋪面狀況指標(Pavement Condition Index, PCI)兩項指標值進行比值分析，找出相關聯之曲線配對，可以長期蒐集與建立大數據，探討本系統之運算因子，發展出最佳道路照明建構模式，供道路鋪面材質重置刨舖生命週期之參考。
研究期間，本論文所研發雙配光曲線設計之LED智慧路燈與控制系統，實際應用於桃園市智慧路燈工程的建設，實測驗證路面乾濕度不同條件下，路燈燈具應有不同的配光特性，才能提升行車安全；並再經由智慧亮度控制，可以兼顧節能與行車安全，對於未來的道路照明工程規劃設計及維護，具有引導性的貢獻。

This dissertation develops an innovative strategy that combines human-centric lighting and intelligent control that uses an adaptive LED street light design together with an imaging luminance meter. Based on the concept of IoT sensor-driven, the imaging luminance meter is used to detect the lighting condition of roadway, with the AI images identification technique, the control system could automatically adjust the adequate light distribution curve of two different types of roadway lighting output, respectively. One of the optical design aims on achieving average illuminance and illuminance uniformity which is suitable when roads are dry and without rain; while another optical design prioritizes average luminnance and luminnance uniformity which is more suitable when roadway surfaces are wet and slippery. That way it could improve the driver's visual clarity effectively, and resolve the problem of driver's inability to correctly identify the road surface conditions and avoid serious glare caused by the slippery road after rain which optimize road safety and visual clarity of passersby at all times, and will eventually help to minimize traffic incidents in rainy nights.
In addition, through the lighting measurement data of the different new and old asphalt road paving materials such as modified asphalt and recycled asphalt, the data can be used to evaluate the lighting performance of the roadways and estimate the pavement reflection coefficient q. At the same time, while analyzing the ratio between the International Roughness Index (IRI) and Pavement Condition Index (PCI) with the luminance reflection coefficient to find out the related curve pairings, big data could be built in the long term to explore the calculation factors of the system. Hence, with the above findings, we are able to develop the best roadway lighting model which matches with the life cycle of roadway pavement and the material applied.
During the research period, the intelligent LED street lighting luminaires and control system with dual light luminous distribution curve design developed in this dissertation was actually applied to the construction of Taoyuan City’s intelligence street lighting project. It was tested and verified that street lightings should have different light luminous distribution characteristics under different conditions of roadway pavement, most likely dry and wet conditions which help improves driving safety. Moreover, through intelligent lighting control, both energy saving and driving safety are attained, which could be considered as a milestone for future roadway lighting planning and design.

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