請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/62656完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 朱浩華(Hao-Hua Chu) | |
| dc.contributor.author | Yi-Hsuan Hsieh | en |
| dc.contributor.author | 謝宜軒 | zh_TW |
| dc.date.accessioned | 2021-06-16T16:06:42Z | - |
| dc.date.available | 2013-07-03 | |
| dc.date.copyright | 2013-07-03 | |
| dc.date.issued | 2013 | |
| dc.date.submitted | 2013-06-13 | |
| dc.identifier.citation | 1. iOnRoad. http://www.ionroad.com/
2. Augmented Driving. https://itunes.apple.com/tw/app/augmented-driving/id366841514?mt=8 3. Coroama, V. The Smart Tachograph – Individual Accounting of Traffic Costs and its Implications. Proc. Pervasive 2006, Springer (2006), pp. 135-152. 4. Flach, T., Mishra, N., Pedrosa, L., Riesz, C., and Govindan, R. CarMA: Towards Personalized Automotive Tuning. Proc. SenSys 2011, ACM Press (2011), 135-148. 5. Define Aggressive Driving. http://www.nhtsa.gov/people/injury/enforce/aggressdrivers/aggenforce/define.html 6. Aggressive Driving: Three Studies. https://www.aaafoundation.org/aggressive-driving-three-studies 7. Kanhere, N.K., Birchfield, S.T. A Taxonomy and Analysis of Camera Calibration Methods For Traffic Monitoring Applications. Trans. Intell. Transport. Sys. 11, 2 (June 2010), 441-452. 8. Wang, T., Cardone, G., Corradi, A., Torresani, L., and Campbell, A.T. WalkSafe: a Pedestrian Safety App for Mobile Phone Users Who Walk and Talk While Crossing Roads. Proc. HotMobile 2012, ACM Press (2012), 5-6. 9. Highways – OpenStreetMap Wiki. http://wiki.openstreetmap.org/wiki/Key:highway 10. Guidance on Road Classification and the Primary Route Network, Department of Transport, Jan, 2012. 11. Chapter 3: Functional Classification. http://www.fhwa.dot.gov/ environment/publications/flexibility/ch03.cfm 12. Salvucci, D. D. and Liu, A. The Time Course of a Lane Change: Driver Control And Eye-Movement Behavior. Transportation Research Part F: Traffic Psychology and Behaviour 5, 2(2002), 123 – 132. 13. Viola, P. and Jones, M. Rapid Object Detection Using a Boosted Cascade of Sim-ple Features. In Proc. CVPR ’01, (2001), volume 1, pages I–511 – I–518 vol.1. 14. Freund, Y., Schapire, R., and Abe, N. A Short Introduction to Boosting. Jour-nal-Japanese Society For Artificial Intelligence, 14(1999), 771-780. 15. Aly, M. Real Time Detection of Lane Markers in Urban Streets. In IEEE Intelligent Vehicles Symposium, June 2008. 16. Histogram Comparison. http://docs.opencv.org/doc/tutorials/imgproc/histograms/histogram_comparison/histogram_comparison.html?highlight=hsv 17. What Driver Really Do Behind the Wheel. http://edition.cnn.com/2007/LIVING/wayoflife/11/20/behind.wheel/index.html 18. Ghsa: survey of the states speeding and aggressive driving, 2012. http://www.ghsa.org/html/publications/pdf/survey/2012_speed.pdf 19. Aggressive Driver Imaging System (ADIS). http://www.matrox.com/imaging/en/press/feature/surveillance/adis/ 20. Johnson, D.A. and Trivedi, M.M. Driving Style Recognition Using a Smartphone as A Sensor Platform. In Proc. IEEE ITSC 2011, 1609-1615. 21. Traffic safety facts. http://www-nrd.nhtsa.dot.gov/ Pubs/811604.pdf 22. NYS DMV - Driver’s Manual - Chapter 8: Defensive Driving. http://dmv.ny.gov/dmanual/chapter08-manual.htm. 23. Szeliski, R. Computer Vision: Algorithms and Applications (1st ed.). Springer-Verlag New York, Inc., New York, NY, USA, 2010. 24. Aggressive Driving. http://www.nhtsa.gov/people/injury/aggressive/ aggproplan-ner/page05.htm 25. Li, K., Lu, M., Lu, F., Lv, Q., Shang, L., and Maksimovic, D. Personalized Driving Behavior Monitoring and Analysis for Emerging Hybrid Vehicles. Proc. Pervasive 2012, Springer-Verlag (2012), 1-19. 26. You, C.-W., Montes-de-Oca, M., Bao, T.J., Lane, N.D., Lu, H., Cardone, G., Tor-resani, T., and Campbell, A.T. CarSafe: A Driver Safety App That Detects Dangerous Driving Behavior Using Dual-Cameras on Smartphones. Proc. UbiComp 2012. ACM (2012), 671-672. 27. Volvo XC60 –City Safety, Fun Drive, GreatSUV/Crossover Design. http://www.volvocars.com/intl/all-cars/volvo-xc60/Pages/default.aspx 28. 台北市100年度交通流量及特性調查, 台北市交通管制工程處 http://163.29.251.188/botedata/交通流量/100年度/html/100年度臺北市交通流量及特性調查.htm | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/62656 | - |
| dc.description.abstract | 「侵犯駕駛」事件對道路安全構成莫大的威脅,本篇論文提出一套手機應用系統來即時偵測並警示駕駛前後的「侵犯駕駛行為」以防止車禍的發生。本系統利用裝置於前方擋風玻璃的對外相機上的後置鏡頭以及裝於車後的後視相機作為感測器,並透過4-core的智慧型手機來處理影像即偵測駕駛周圍重要的4個「侵犯駕駛」事件。本論文設計、實作和描述了一個手機應用系統,實驗涵蓋19個不同的駕駛,總共駕駛226.3公里,共計376個外部 「侵犯駕駛」事件,實驗結果平均precision 為85% 而 recall 為84%。另外本論文亦展示了4-core的智慧型手機可以有效的平均只花175毫秒的處理時間來完成運算,因此可以有效的給予駕駛即時危險警示。 | zh_TW |
| dc.description.abstract | Aggressive driving has become one serious threat to road safety. This thesis pre-sents a near real-time phone-based system that detects and informs drivers to avoid car accidents caused by front and rear aggressive driving events. The rear camera of a smartphone and a rearview camera suite are deployed as vision sensors to capture front and rear images of a vehicle. The techniques of image processing are adopted on a smartphone to detect 4 significant aggressive driving events near our vehicles. We de-sign and implement the phone-based system, and conduct a real world experiment in-volving 19 different participates and 226.3 km of driving trips, and 376 aggressive driving events. The average precision and recall of identifying aggressive driving events are 85% and 84% respectively. We also show that the processing speed of our system is efficient on a 4-core smartphone with low 175 ms latency, so it enables instant feedback to drivers. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T16:06:42Z (GMT). No. of bitstreams: 1 ntu-102-R00922022-1.pdf: 814997 bytes, checksum: 6561cda11cf824e6ff6fc3a9de208997 (MD5) Previous issue date: 2013 | en |
| dc.description.tableofcontents | ACKNOWLEDGEMENT I
ABSTRACT II 中文摘要 III CONTENTS IV LIST OF FIGURES VI LIST OF TABLES VII CHAPTER 1:INTRODUCTION 1 1.1 MOTIVATION 1 1.2 PROBLEM AND PROPOSED SOLUTION 1 1.3 CONTRIBUTION 2 CHAPTER 2:SYSTEM ARCHITECTURE & DESIGN 3 2.1 後視影像接收器 4 2.2 多核心影像處理 5 2.2.1 距離測量 5 2.2.2 車道模型抽取 7 2.2.3 影像處理調度 8 2.2.4 推論輸出調節器 8 2.3 車輛狀態分類 8 2.3.1 速度偵測 9 2.3.2 道路型別辨識 9 2.3.3 交叉路口定位 9 2.4 侵犯駕駛事件判斷引擎 10 2.4.1 不適當的車輛穿越(IP) 10 2.4.2 跟車(TG) 12 2.4.3 被跟車 (BT) 12 2.4.4 前方車輛突然剎車 (SB) 12 2.5 駕駛回饋 13 2.6 系統實作 14 CHAPTER 3:EVALUATION 15 3.1 DATA COLLECTION 15 3.1.1 DAILY DRIVING DATA SET 15 3.1.2 CONTROLLED DRIVING DATA SET 16 3.2 侵犯性駕駛事件的偵測準確度 17 3.3 多核心影像處理和車輛狀態分類的準確率 18 3.4 影像大小與幀率 19 3.5 駕駛在不同路段的開車危險度 20 CHAPTER 4:RELATED WORK 22 CHAPTER 5:結論和未來發展 24 參考資料 25 | |
| dc.language.iso | zh-TW | |
| dc.subject | 影像處理 | zh_TW |
| dc.subject | 手機系統 | zh_TW |
| dc.subject | 侵犯性駕駛 | zh_TW |
| dc.subject | 近於即時系統 | zh_TW |
| dc.subject | aggressive driving | en |
| dc.subject | image process | en |
| dc.subject | near real-time system | en |
| dc.subject | phone-based system | en |
| dc.title | 利用手機即時偵測車輛周圍的侵犯駕駛行為 | zh_TW |
| dc.title | A Phone-based System to Measure Driving Aggressiveness around Vehicles | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 101-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 黃寶儀(Polly Huang) | |
| dc.contributor.oralexamcommittee | 陳文村(Wen-Tsuen Chen),曾煜棋(Yu-Chee Tseng),陳伶志(Ling-Jyh Chen) | |
| dc.subject.keyword | 侵犯性駕駛,手機系統,近於即時系統,影像處理, | zh_TW |
| dc.subject.keyword | aggressive driving,phone-based system,near real-time system,image process, | en |
| dc.relation.page | 26 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2013-06-14 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| 顯示於系所單位: | 資訊工程學系 | |
文件中的檔案:
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-102-1.pdf 未授權公開取用 | 795.9 kB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。
