臺灣博碩士論文加值系統

隨著現代智慧城市的快速發展，車聯網系統技術在交通管理中的應用日益廣泛，如自駕車系統、車況分析、遠程監控系統、導航路線規劃等，因此希望在安全駕駛之外，可以有效解決因A柱或龐大車身死角而導致的意外並提供所有用路人一個安全，且即時的智慧斑馬線系統。本文提出了一種基於車聯網架構的智慧斑馬線系統，主要提高行人穿越馬路的安全性和交通效率。
本系統透過ESP32模組實時收集行人RSSI(訊號接收強度)值，並通過MQTT協議將這些數據傳輸到樹莓派進行定位運算，為了改善一般單點定位與平均定位，使用更加精確的權重定位法及卡爾曼濾波器(Kalman filter)，並上傳至MySQL資料庫讓附近車輛得知行人資訊。此外，斑馬線上安裝設有LED燈，用於提示周邊車輛注意行人；紅綠燈上還設有可視介面，即時顯示行人位置，進一步提高行人安全性，最後本系統會經過多次實驗測試，來驗證系統是否能提高行人的整體交通安全性。
此系統利用車輛對行人(Vehicle to Passenger, V2P)和車輛與基礎設施（Vehicle-to-Infrastructure, V2I）之間的信息互通，實現智能化車聯網交通管理。

With the rapid development of modern smart cities, the application of Internet of Vehicles (IoV) technology in traffic management has become increasingly widespread. This includes autonomous driving systems, vehicle condition analysis, remote monitoring systems, navigation route planning, and more. Beyond ensuring safe driving, there is a desire to effectively address accidents caused by blind spots from the A-pillars or large vehicle bodies and to provide all road users with a safe and timely intelligent zebra crossing system. This paper proposes an intelligent zebra crossing system based on the IoV architecture, primarily aimed at enhancing pedestrian safety and traffic efficiency.
This system uses ESP32 modules to collect real-time RSSI (Received Signal Strength Indicator) values from pedestrians and transmits this data to a Raspberry Pi for positioning calculations via the MQTT protocol. To improve on typical single-point and average positioning, more precise weighted positioning methods and the Kalman filter are employed, and the data is uploaded to a MySQL database to inform nearby vehicles of pedestrian information. Additionally, LED lights installed on the zebra crossing alert surrounding vehicles to pedestrians; visual interfaces on traffic lights display the pedestrians' positions in real-time, further enhancing pedestrian safety. Finally, this system will undergo multiple experimental tests to verify whether it can improve overall traffic safety for pedestrians.
This system leverages Vehicle-to-Passenger (V2P) and Vehicle-to-Infrastructure (V2I) communication to achieve intelligent IoV traffic management.

摘要...i
Abstract...ii
誌謝...iii
目錄...iv
表目錄...vi
圖目錄...vii
第1章 緒論...1
1.1 研究背景...1
1.2 研究動機與目的...1
1.3 論文架構...2
第2章 文獻回顧...3
2.1 車聯網(Internet of Vehicles, IoV)...3
2.2 RSSI(接收訊號強度)...5
2.3 平均定位...8
2.4 權重定位...8
2.4.1 歸一化...9
2.4.2 Softmax...10
2.5 卡爾曼濾波器...10
2.6 MQTT...11
2.7 MySQL...13
2.7.1 PhpMyAdmin...14
2.8 PyQT5...15
第3章 實驗規劃與方法...17
3.1 軟硬體介紹...17
3.2 系統架構圖...21
3.3 系統流程圖...22
3.4 MQTT架設與執行流程...23
3.5 定位方法...26
3.5.1 實驗環境...26
3.5.2 手機訊號篩選...28
3.5.3 RSSI訊號測量...30
3.5.4 平均定位...34
3.5.5 權重定位...35
3.5.6 卡爾曼定位(Kalman filter)...37
3.6 LED燈條控制方法...39
3.7 OBU資料庫建置流程...41
3.8 定位可視畫介面建置流程...43
第4章 實驗結果...45
4.1 斑馬線行人定位實驗結果...45
4.1.1 不同定位的比較實驗...45
4.1.2 斑馬線行人提示燈與可視化介面...50
4.1.3 OBU資料庫...52
第5章 結論與未來研究...54
參考文獻...55
Extended Abstract...58

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