隨著感測技術、軟硬體、人工智慧之發展，自駕車產業之相關研究在過去數十年間快速地盛行，為使自駕車可於道路上安全行駛，必須藉助先進之駕駛輔助系統(Advanced Driver Assistance Systems, ADAS)，持續掌握發生於道路及周遭環境中的各種狀況。除了三維高精地圖、感測器接收之資訊與車聯網訊息外，亦需要更為周全地掌握即時路況及其他可能影響道路交通之各種資訊，以形成更好的駕駛決策。本研究以國際間廣泛採用之LDM(Local Dynamic Map)架構為切入點，專注於其四層架構中具有動態變化之資訊，LDM 是一個將即時自動駕駛車輛與交通資訊整合到高精地圖中實現動態地圖數據共享的資料庫，其架構具體規劃了智慧交通系統所需建立及傳輸之各類動靜態資訊。基於LDM架構，本文探討自駕車對於半靜態半動態資訊的需求，並且根據內政部「高精地圖標準及智能移動測繪技術發展工作案(109-110)」所研擬之自駕車高精地圖輔助與事件資料標準(草案)，分析其設計策略與討論可能之應用方式。此資料標準以5W1H (What、Where、Who、When、Why與How)觀點設計與規劃自駕車所需半靜態及半動態資訊之描述架構及內容，稱之為「事件」資料，期望統合國內不同來源及格式之半靜態與半動態資訊。除了設計決策建議相關屬性以可以滿足自駕車決策系統需求外，也明確制定各種時間、空間與更新等要素，藉此滿足自駕車對於各道路相關事件高精度、正確整合及動態更新之需求。
基於交通道路相關的事件將對於整體之運作環境構成時間與空間之約制，連帶影響自駕車在路徑規劃上之選擇，因此本文由同時考量時間與空間約制條件之三維時空系統架構，分析半靜態與半動態資訊如何被融入駕駛行為之判斷，並由時空約制觀點建立明確的路徑規劃決策之規則邏輯，以提供自駕車未來在路徑規劃決策機制之參考。基於本研究建立之決策規則邏輯，以GIS軟體模擬自駕車在不同情境下所面臨的路徑規劃決策，證明自駕車可以在持續更新事件資訊之條件下，採用較佳的規劃路徑。以本研究成果而言，要體現LDM之發展概念進一步結合各專業單位所提供之即時資訊為有效且必要之策略，建立納入決策、空間與時間考量之標準事件描述架構將有利於自駕車產業跨域相關資源之整合發展，以及提供具有明確時空約制之事件資料，將有助於自駕車之行程規劃朝強化智慧決策之目標推進。

With the rapid advance of sensor, positioning and communication technology, autonomous cars industry has rapidly developed in recent years. The safe driving of autonomous cars not only relies on the Advanced Driver Assistance Systems (ADAS), but also needs to have an up-to-date understanding about the variety of events occurred on the roads. Based on the Local Dynamic Map (LDM) concept, this research intends to discuss how road event data established following a standardized schema can be used to assist the decision-making of autonomous cars. The examined standard of event data was proposed by the Ministry of the Interior (MOI). In addition to designing the required attributes of updating the road event status and providing decision-making suggestions to meet the needs of operating autonomous cars, the designed schema also defines various attributes of time and space to meet the requirements of high-precision, correct integration, and dynamic updating of road status. This research considers four current data services as the data source. The mapping between these four data sources and the standardized event schema is analyzed to facilitate the conversion between data source and the required road events. Based on the analyzed results, we propose a guideline of strategy for converting the heterogeneous road event data to the standardized schema. With such standardization processes, the spatio-temporal constraints can be established accordingly upon acquired events from heterogeneous sources. This research further proposes a workflow for aiding the automatic route planning and selection for autonomous cars. According to the proposed workflow, various simulated scenarios are tested with GIS software to demonstrate the feasibility of the proposed mechanism.

T 客邦(2013)。Google 在台成立「Google 台灣災害應變資訊平台」，建立更完善的防災系統。檢自：https://www.inside.com.tw/article/2651-google-set-up-a-google-taiwan-disaster-response-platform (Aug. 25, 2022)
內政部(2021)。高精地圖標準及智能移動測繪技術發展工作案(109-110)，第4期成果報告。
王冠尹(2018)。適應性與啟發式RRT演算法在無人載具導控之應用 (碩士論文)。取自臺灣博碩士論文系統。(系統編號105NTU05489120)。
台南市政府體育局(2022)。2022臺南古都半馬交通管制時間圖。檢自：https://sports.tn.edu.tw/html/news?nid=62189d36761f8 (April 4, 2022)
台灣車聯網產業協會之 TCROS 工作小組(2021)。號誌控制器與車聯網路側設施間資通訊標準。V1.0。
台灣資通產業標準協會(TAICS) (2020)。高精地圖圖資內容及格式標準。v1.1。
交通部(2016)。交通路網數值圖105年版對外發行使用手冊。
交通部運研所(2004)。台灣地區智慧型運輸系統綱要計畫－2003-2010(研究報告書)
交通部運輸研究所(2020)。道路交通事件資料標準(草案)V0.3。
交通部運輸資訊組(2004)。交通路網數值地圖之維護更新(一)。檢自: https://www.iot.gov.tw/cp-78-12315-cd269-1.html (June 6, 2022)
交通部運輸資訊組，呂思慧、陳其華(2017)。ITS 發展方向及策略探討。
江凱偉、曾芷晴(2019)。不需駕駛也能輕鬆上路－淺談自駕車與高精地圖。科學月刊，201905(449)。
余至浩(2018)。【深度剖析無人車運作原理】AI如何學習開車。檢自：https://www.ithome.com.tw/news/125895 (Oct. 28, 2021)
李崗(2019)。國際車輛自動駕駛技術發展。土木水利，46(2)，11-17。
李綱(2019)。自駕車商業化的挑戰。檢自：https://ictjournal.itri.org.tw/Content/Messagess/contents.aspx?&MmmID=654304432122064271&CatID=654313611331661503&MSID=1036501243231065271 (Jan. 30, 2022)
沈怡如(2020)。全球ITS政策及重點發展方向。機械工業雜誌，442，69-73
周家慶(2008)。地理資訊系統之路網應用實例。交通部運研所。
林宗佑(2019)。改良式 A* 演算法於動態環境路徑規劃與避障之應用(碩士論文)。取自臺灣博碩士論文系統。(系統編號106NTU05499041)。
知識力、林貞妤(2019)。台灣高精地圖選定OpenDrive + Extension！積極與第三方業者合作！檢自：https://www.ansforce.com/post/S1-p1462 (Feb. 12, 2022)
徐珮晴、李育華、江凱偉(2019)。高精地圖輔助車載衛星定位偵測與改善都市多路徑效應及 NLOS 誤差影響。航測及遙測學刊，24(2)，123-133。
財團法人車輛研究測試中心(2020)。科技專案成果。ADAS研發整合再進化 道路駕駛更安心。檢自: https://www.moea.gov.tw/MNS/doit/achievement/Achievements2.aspx?menu_id=5390&ac_id=1558 (Feb. 12, 2022)
財團法人車輛研究測試中心黃威陞(2019)。智慧時代來臨車聯網技術的選擇。檢自: https://www.artc.org.tw/chinese/03_service/03_02detail.aspx?pid=13371 (Feb. 18, 2022)
高得欽、梁珮蓉、陳澤民、陳柏宇(2019)。感知融合技術於自駕車之應用。檢自：https://ictjournal.itri.org.tw/content/Messagess/contents.aspx?&MmmID=654304432061644411&CatID=654313611255143006&MSID=1036474756413211611 (Mar. 2, 2022)
高敬原(2021)。自駕技術離商用不遠了？「最懂自駕車」的詹景堯：先學習接受科技不完美。檢自: https://www.bnext.com.tw/article/61531/autonomous-cars-ev (Mar. 2, 2022)
國家災害防救科技中心，2015。共通示警協議標準，第一版。
戚铭尧、吴涛、张新(2015)。车辆路径问题：从时间地理学的视角。地球信息科学学报，17(1)，22-30。
陳品均(2013)。針對動態路徑規劃之D++演算法研究及其應用(博士論文)。取自臺灣博碩士論文系統。(系統編號101NCTU5489093)
陳敬典(2018)。自動駕駛車發展現況與未來趨勢。
陳敬典(2022)。全球自駕車產業現況與發展趨勢。
智慧運輸技術中心(2022)。淺談國際推動車聯網與自駕車之現況-以歐盟為例。檢自：https://www.ceci.org.tw/modules/article-content.aspx?s=19&i=503 (June 6, 2022)
曾芷晴、徐珮晴、張清鴻、李育華、江凱偉、王靚琇、黃鉅富、吳俊毅(2019)。自駕車使用之高精度地圖規範擬定與資料蒐集處理之可行性評估。航測及遙測學刊，24(3)，173-182。
超越車訊‧Spec R‧兩輪誌(2022)。【超越車訊】【特別企劃】四大ADAS先進駕駛輔助系統總評比，Ford Co-Pilot 360、Mazda i-ACTIVSENSE、Toyota TSS 2.0、VW IQ.DRIVE。影片網址：https://www.youtube.com/watch?v=U7F7juJ4Y2g&ab_channel=%E3%80%90TaiwanMotorGroup%E3%80%91%E8%B6%85%E8%B6%8A%E8%BB%8A%E8%A8%8A%E2%80%A7SpecR%E2%80%A7%E5%85%A9%E8%BC%AA%E8%AA%8C (June 28, 2022)
馮道亨、李文騫、黃惠隆、游上民(2017)。智慧運輸之發展趨勢。
黃章杰(2016)。淺談先進駕駛輔助系統的源起與發展。
黃樑傑(2018)。車輛智慧化現況與自動駕駛進展。零組件雜誌。201804 (318)，44-48。
新竹縣政府(2021)。【文化科技智慧城】自駕巴士駛進日常 竹縣智慧交通新里程。檢自：https://www.cw.com.tw/article/5117930 (May 22, 2022)
蘇文彬(2021)。交通部運輸資料流通平臺TDX再進化，和11家業者聯手打造資料市集，要整合公私部門數據創造更大價值。檢自：https://www.ithome.com.tw/news/142077 (May 12, 2022)
Allen, J. F. (1983). Maintaining knowledge about temporal intervals. Communications of the ACM, 26(11), 832-843.
Amanda Leicht Moore. (2022). Three new Maps updates to help plan your next adventure. Retrieved from : https://blog.google/products/maps/three-new-maps-updates-to-help-plan-your-next-adventure/ (Aug. 8, 2022)
Amit’s A* Pages (n.d.). Retrieved from http://theory.stanford.edu/~amitp/GameProgramming/ (Jan. 24, 2022)
ArcGIS Pro Help(n.d.). Algorithms used by the ArcGIS Network Analyst extension. Retrieved from : https://pro.arcgis.com/en/pro-app/2.8/help/analysis/networks/algorithms-used-by-network-analyst.htm (May 25, 2022)
Ashish. (2022). How Does Google Maps Know About Traffic Conditions? Retrieved from https://www.scienceabc.com/innovation/how-does-google-maps-know-about-traffic-conditions.html (Jul 8, 2022)
Barbaresso, J., Cordahi, G., Garcia, D., Hill, C., Jendzejec, A., Wright, K., & Hamilton, B. A. (2014). USDOT’s Intelligent Transportation Systems (ITS) ITS strategic plan, 2015-2019.
Bria Rosenberg. (2017). 無人駕駛車的益處。檢自：https://valientemott.com/auto-collisions/self-driving-cars-pros-and-cons/ (May 7, 2022)
Campbell, S., O'Mahony, N., Krpalcova, L., Riordan, D., Walsh, J., Murphy, A., & Ryan, C. (2018). Sensor Technology in Autonomous Vehicles: A review. Paper presented at the 2018 29th Irish Signals and Systems Conference (ISSC).
CHEN S, Z., HU J, L., & SHI, Y. (2017).Vehicle-to-Everything (v2x) Services Supported by LTE-Based Systems and 5G. IEEE Communications Standards Magazine, 1(2), 70-76.
Daniel, A., Paul, A., Ahmad, A., & Rho, S. (2016). Cooperative intelligence of vehicles for intelligent transportation systems (ITS). Wireless Personal Communications, 87(2), 461–484.
Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik 1(1), 269–271.
Dong, J., & Shen, G. J. (2013). A weight-based road impedance function model. Paper presented at the Advanced Materials Research.
Eiter, T., Füreder, H., Kasslatter, F., Parreira, J., & Schneider, P. (2018). Towards a Semantically Enriched Local Dynamic Map. International Journal of Intelligent Transportation Systems Research, 17. doi:10.1007/s13177-018-0154-x
ETSI, T. (2011). 102 863 (v1. 1.1): Intelligent transport systems (ITS); vehicular communications; basic set of applications; local dynamic map (LDM); rationale for and guidance on standardization. ETSI: Valbonne Sophia Antipolis, France.
Faisal, A., Kamruzzaman, M., Yigitcanlar, T., & Currie, G. (2019). Understanding autonomous vehicles. Journal of transport and land use, 12(1), 45-72.
Fenton, R. E., & Mayhan, R. J. (1991). Automated highway studies at the Ohio State University-an overview. IEEE Transactions on Vehicular Technology, 40(1), 100-113. doi:10.1109/25.69978
Fu, M., Li, J., & Deng, Z. (2004). A Practical Route planning Algorithm for Vehicle Navigation System. Paper presented at the Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No. 04EX788).
Geisberger, R., Sanders, P., Schultes, D., & Delling, D. (2008). Contraction Hierarchies: Faster and Simpler Hierarchical Routing in Road Networks. Paper presented at the International workshop on experimental and efficient algorithms.
Gruyer, D., Magnier, V., Hamdi, K., Claussmann, L., Orfila, O., & Rakotonirainy, A. (2017). Perception, information processing and modeling: Critical stages for autonomous driving applications. Annual Reviews in Control, 44, 323-341.
Hägerstrand, T. (1970). What about people in Regional Science? Papers of the Regional Science Association, 24(1), 6-21. Retrieved from https://doi.org/10.1007/BF01936872. doi:10.1007/BF01936872
Hart, P. E., Nilsson, N. J., & Raphael, B. (1972). Correction to" a formal basis for the heuristic determination of minimum cost paths". ACM SIGART Bulletin(37), 28-29.
Hussain, R., & Zeadally, S. (2018). Autonomous Cars: Research Results, Issues, and Future Challenges. IEEE Communications Surveys & Tutorials, 21(2), 1275-1313.
ISO. (2015). ISO/TR 17424 : Intelligent transport systems —Cooperative systems — State of the art of Local Dynamic Maps concepts. Technical report.
ISO. (2015). ISO/TS 18750 : Intelligent transport systems – Cooperative systems - Definition of a global concept for Local Dynamic Maps. Technical report.
ITS America. (2021). A BETTER FUTURE TRANSFORMED BY INTELLIGENT MOBILITY.
Jan, G., Lee, M., Hsieh, S., & Chen, Y.-Y. (2009). Transportation network navigation with turn penalties.
Johann Lau. (2020). 我們如何使用 AI 預測車流量、決定最佳路線。檢自：https://taiwan.googleblog.com/2020/09/maps-how-we-use-AI-to-predict-traffic-and-determine-routes%20.html (Aug. 20, 2022)
Kiela, K., Barzdenas, V., Jurgo, M., Macaitis, V., Rafanavicius, J., Vasjanov, A., . . . Navickas, R. (2020). Review of V2X–IoT Standards and Frameworks for ITS Applications. Applied Sciences, 10(12), 4314.
Koenig, S., & Likhachev, M. (2002). D* Lite. Paper presented at the Eighteenth national conference on Artificial intelligence, Edmonton, Alberta, Canada.
Lin, Y., Wang, P., & Ma, M. (2017). Intelligent transportation system (ITS): Concept, challenge and opportunity. Paper presented at the 2017 ieee 3rd international conference on big data security on cloud (bigdatasecurity), ieee international conference on high performance and smart computing (hpsc), and ieee international conference on intelligent data and security (ids).
Liu, J., Xiao, J., Cao, H., & Deng, J. (2019). The Status and Challenges of High Precision Map for Automated Driving. Paper presented at the China Satellite Navigation Conference.
Nizomjon, K., & Oh, R.-D. (2017). Middleware structure using Local Dynamic Map within its environment.
Pendleton, S. D., Andersen, H., Du, X., Shen, X., Meghjani, M., Eng, Y. H., . . . Ang Jr, M. H. (2017). Perception, Planning, Control, and Coordination for Autonomous Vehicles. Machines, 5(1), 6.
Peter J. Leiss. (2018, September 18). The Functional Components of Autonomous Vehicles - Expert Article. Retrieved from https://www.robsonforensic.com/articles/autonomous-vehicles-sensors-expert/ (Oct 18, 2021)
Pin. (2021). 告別老司機》迎接自駕時代，各國法規都準備好了嗎？檢自: https://technews.tw/2021/04/20/are-laws-and-policies-ready-for-the-era-of-self-driving/ (Mar. 8, 2022)
Poggenhans, F., Pauls, J.-H., Janosovits, J., Orf, S., Naumann, M., Kuhnt, F., & Mayr, M. (2018). Lanelet2: A high-definition map framework for the future of automated driving. Paper presented at the 2018 21st international conference on intelligent transportation systems (ITSC).
Rebecca Bellan. (2022). Waymo to begin charging for robotaxi rides in San Francisco. https://techcrunch.com/2022/02/28/waymo-to-begin-charging-for-robotaxi-rides-in-san-francisco/ (May 17, 2022)
SAE. (2021). SAE Levels of Driving Automation. Retrived from：https://www.sae.org/blog/sae-j3016-update (Oct. 18, 2021)
Sanders, P., & Schultes, D. (2006). Engineering Highway Hierarchies. Paper presented at the European Symposium on Algorithms.
Schrank, D., Eisele, B., Lomax, T., & Bak, J. (2015). 2015 urban mobility scorecard.
Sepulcre, M., Mittag, J., Santi, P., Hartenstein, H., & Gozalvez, J. (2011). Congestion and Awareness Control in Cooperative Vehicular Systems. Proceedings of the IEEE, 99(7), 1260-1279.
Shahzada, A., & Askar, K. (2011). Dynamic vehicle navigation: An A* algorithm based approach using traffic and road information. Paper presented at the 2011 IEEE International Conference on Computer Applications and Industrial Electronics (ICCAIE).
Shaw, S. L., Yu, H., & Bombom, L. S. (2008). A space‐time GIS approach to exploring large individual‐based spatiotemporal datasets. Transactions in GIS, 12(4), 425-441.
Shladover, S. E. (2018). Connected and automated vehicle systems: Introduction and overview. Journal of Intelligent Transportation Systems, 22(3), 190-200.
Singh, B., & Gupta, A. (2013). Advancements in Intelligent Transportation Systems: A Review. Paper presented at the Proceedings of International Conference on Advanced Trends in Engineering and Technology (ICATET), Jaipur.
Singh, B., & Gupta, A. (2015). Recent trends in intelligent transportation systems: a review. Journal of Transport Literature, 9, 30-34.
Singh, S. (2018). Critical reasons for crashes investigated in the national motor vehicle crash causation survey (Traffic Safety Facts Crash• Stats. Report No. DOT HS 812 506). Washington, DC: National Highway Traffic Safety Administration.
Stentz, A. (1994). Optimal and efficient path planning for partially-known environments. Paper presented at the Proceedings of the 1994 IEEE International Conference on Robotics and Automation.
Stentz, A. (1995). The focussed d^* algorithm for real-time replanning. Paper presented at the IJCAI.
Sugimoto, Y., & Kuzumaki, S. (2019). SIP-adus: an update on japanese initiatives for automated driving. In Road Vehicle Automation 5 (pp. 17-26): Springer.
TOM DAHLSTRÖM. (2021). The road to everywhere: are HD maps for autonomous driving sustainable? Retrieved from：https://www.autonomousvehicleinternational.com/features/the-road-to-everywhere-are-hd-maps-for-autonomous-driving-sustainable.html (Oct. 18, 2021)
VALIENTE MOTT. (n.d.). SELF-DRIVING CARS: PROS AND CONS. Retrived from：https://valientemott.com/auto-collisions/self-driving-cars-pros-and-cons/ (Jan. 3, 2022)
Wariishi, K., & Kinjo, H. (2018). Development of Dynamic Map for Automated Driving and its Potential to be Next-Gen Industrial Infrastructure. In Mitsui & Co. Global Strategic Studies Institute Monthly Report.
World Health Organization (WHO). (2021). Road traffic injuries. Retrieved from https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries (Nov. 13, 2021)
Yan, X., Zhang, H., & Wu, C. (2012). Research and Development of Intelligent Transportation Systems. Paper presented at the 2012 11th International Symposium on Distributed Computing and Applications to Business, Engineering & Science.
Ying, S., & Yang, Y. (2008). Study on vehicle navigation system with real-time traffic information. Paper presented at the 2008 International conference on computer science and software engineering.
Yu, Y., & Li, B. (2009). Real-time traffic data management for dynamic vehicle navigation system. Paper presented at the 2009 17th International Conference on Geoinformatics.
Zhao, Y. (1997). Vehicle location and navigation systems.
Ziebinski, A., Cupek, R., Grzechca, D., & Chruszczyk, L. (2017). Review of advanced driver assistance systems (ADAS). Paper presented at the AIP Conference Proceedings.