近年來，石油危機空氣污染嚴重影響人類經濟成長及生活環境，如何減少交通工具造成的環境污染將是重要議題。由於科技不斷的進步，消費者對汽車使用上的需求也從單純的運輸功能擴增到對所有乘客的安全及便利的要求。智慧型運輸系統（Intelligent Transportation Systems, ITS）便因此而產生，ITS即是運用電子、控制、通訊與資訊科技，結合交通管理的理論與實務，提昇運輸系統的服務功能，是目前世界先進國家致力研究發展的方向。智能交通系統的主要目的是不管在車裡、車外，都能利用電子、通訊、資訊與感測等技術，將路況、車輛、駕駛者的資訊加以整合與管理，為駕駛者提供即時資訊，從而增進運輸系統的安全、效率及舒適性。
欲達到上述目標，我們必須結合各種不同的行車資訊。本研究重點在於OBU (On Board Unit) 內軟硬體系統之整合開發，負責與RSU (Roadside Unit) 進行交通訊息之傳遞與處理，如紅綠燈路口資訊或車隊等待長度。OBU利用車內感測器蒐集即時的行車車況，並建立影像車輛辨識系統來偵測前方車輛距離，以增加車輛行駛的安全性，並利用節能駕駛建議演算法進而降低車輛之油耗。我們將Panda Board作為OBU (On Board Unit) 負責蒐集感測器的資料，並利用Ad-hoc模式的無線網路建立VANET (Vehicular Ad-hoc Network) 場景，與RSU進行訊息之傳遞與處理。最後結果部份除了實踐OBU在VANET場景中建立節能駕駛建議系統以及影像處理技術建立前方碰撞預警系統，另外也針對本研究使用的節能演算法模擬在車輛燃油的消耗並作相關的討論。

In recent years, the oil crisis and air pollution seriously affect human economic growth and living environment. How to reduce the pollution caused by vehicles is an important issue. With the advancement of science and technology, consumers expect vehicles to equip with more functionalities for not only safety, but also comfort and convenience. The ITS (Intelligent Transportation Systems) was created for this purpose. ITS uses traffic management and combines all kinds of science and technology (e.g., electronics, controllers, communications) to improve our transportation systems. This is the current trend in advanced countries, which are committed to researching and developing. ITS collects the information from road, vehicle and people by electronics, communications and sensing technology, etc. in order to provide drivers the real-time information, and promote transportation system safety, efficiency, and comfort.
To attain the objectives listed above, we must combine all different kinds of vehicular information. Our research aims to develop the hardware and software system of the OBU (On-Board Unit), which is in charge of exchanging and processing the traffic messages (e.g., traffic light information, vehicles waiting queue) with RSUs. In addition, OBU not only collects the vehicle condition by using on-board sensors and constructs image-processing system to increase the safety of driving, but also uses ECO-driving algorithm to decrease the fuel consumption.
We use Panda Board as OBU (On Board Unit) which is responsible for integrating information of on-board sensors and communicating with RSUs by using Ad-hoc mode wireless network in the VANET (Vehicular Ad-hoc Network) scenario. In the results, we not only construct FCWS (Forward Collision Warning System) by using image-processing technology, but also implement the ECO-driving assistance system in the VANET scenario. Moreover, we simulate the basic traffic situations with our proposed ECO-driving algorithm and analyze fuel consumption of the vehicles.

[1] IEA, CO2 Emissions from Fuel Combustion 2012 - Highlights (2012 edition), Luxembourg, October 2012.
[2] Statistics of Ministry of Transportation and Communication R.O.C., http://www.motc.gov.tw
[3] Y. Kudoh, DSRC standards for multiple applications, In Proceedings of 11th world congress on ITS, 2004.
[4] Ke-Chian Su, Wei-Ling Chang and Yao-Hsin Chou, “Vehicle-to-Vehicle Communication System through Wi-Fi Network Using Android Smartphone,” 2012 International Conference on in Connected Vehicles and Expo (ICCVE), pp. 191-196, April 2012.
[5] Evry France, H.T. Luu, L. Nouveliere and S. Mammar, “Energy Saving and Safe Driving Assistance System for Light Vehicles: Experimentation and Analysis,” 2012 9th IEEE International Conference on in Networking, Sensing and Control (ICNSC), pp. 346-351, Dec. 2012.
[6] Syuan-Yi Chen, Wei-Yao Chou and Yi-Chun Lin “Intelligent eco-driving suggestion system based on vehicle loading model,” 2012 12th International Conference on ITS Telecommunications (ITST), pp. 558-562, 5-8 Nov. 2012.
[7] “Intelligent transport systems -- Forward vehicle collision warning systems
-Performance requirements and test procedures,” ISO 15623:2013.
[8] G. Johansson and K. Rumar “Driver’s Brake Reaction Times,” Hum. Factors. 1971, 13 (1) pp. 23-27.
[9] OpenCV Reference Manual. URL:
http://docs.opencv.org/doc/tutorials/tutorials.html
[10] Movsim.org. URL:
http://www.h2063376.stratoserver.net/movsim/
[11] OMAPTM 4 PandaBoard System Reference Manual. URL: http://pandaboard.org/sites/default/files/board_reference/EA1/Panda_Board_Spec_REVEA1_04.pdf
[12] MSP430x5xx Family User's Guide. URL: http://www.ti.com/lit/ug/slau208m/slau208m.pdf
[13] Hong Tu Luu, L. Nouveliere and S. Mammar, “Ecological and safe driving assistance system: Design and strategy,” 2010 IEEE Intelligent Vehicles Symposium (IV), pp. 129-134, 21-24 June 2010.
[14] J. Daniel, J. Lauffenburger, M. Basset, R. Attia and R. Orjuela “Reference generation and control strategy for automated vehicle guidance,” 2012 IEEE Intelligent Vehicles Symposium (IV), pp. 389-394, 3-7 June 2012.
[15] S. Zeadally, R. Hunt, Y.-S. Chen, A. Irwin “Vehicular ad hoc networks (VANETS): status, results, and challenges,” Telecommun Syst DOI 10.1007/s11235-010-9400-5, Springer Science Business Media, LLC 2010.
[16] W. H. Lee, Y. C. Lai, and P. Y. Chen, “Decision-tree based green driving suggestion system for carbon emission reduction,” 2012 12th International Conference on ITS Telecommunications (ITST), pp. 486-491, 5-8 Nov. 2012.
[17] W. H. Lee, Y. C. Lai, and P. Y. Chen, “A Study on Energy Saving and CO2 Emissions Reduction on Signal Countdown Extension by Vehicular Ad-Hoc Network,” 2014 IEEE Transactions on Vehicular Technology, pp. 1, 11 Feb. 2014.
[18] M. Alsabaan, K. Naik, T. Khalifa, and A. Nayak, “Vehicular Networks for reduction of fuel consumption and CO2 Emission,” 2010 8th IEEE International Conference on Industrial Informatics (INDIN), pp.671-676, 13-16 July 2010.
[19] J. L. Shieh, C. Y. Lu, and W. H. Lee, “VANET-based Coordinated Signal Control in a Local Network for Minimizing CO2 Emissions and Fuel Consumption,” 9th International Conference on Computing Technology and Information Management, 2013.
[20] K. Katsaros, R. Kernchen, M. Dianati and D. Rieck, “Performance study of a Green Light Optimized Speed Advisory (GLOSA) application using an integrated cooperative ITS simulation platform,” Wireless Communications and Mobile Computing Conference (IWCMC), pp. 918-923, 2011.
[21] H. Rakha, K. Ahn, and A. Trani, “Comparison of MOBILESa, MOBILE6, VT-MICRO, and CMEM Models for Estimating Hot-Stabilized Light-Duty Gasoline Vehicle Emissions,” Canadian Journal of Civil Engineering, vol. 30, pp. 1010-1021, 2003.
[22] P. Wararkar and S.S.Dorle “Vehicular Ad-hoc Networks Handovers with Metaheuristic Algorithms,” The International Conference on Electronic Systems, Signal Processing and Computing Technologies, pp.32-40, Feb. 2014.
[23] G. Challita, S. Mousset, F. Nashashibi and A. Bensrhair, “An application of V2V communications : Cooperation of vehicles for a better car tracking using GPS and vision systems,” IEEE Vehicular Networking Conference (VNC), pp. 1-6, Oct. 2009.
[24] C. Dobre, C. Fratila, and L. Iftode, “An Approach to Evaluating Usability of VANET Applications,” the International Conference on Wireless Communications and Mobile Computing, pp.801-807, 2011.
[25] R. E.C.M. van der Heijden and V. A.M.J. Marchau, “Intelligent Transport Systems (ITS) and Driving Behaviour: Setting the Agenda,” the International Conference on Systems, Man and Cybernetics, pp. 4003-4010, Oct. 2004.
[26] B. Aytekin and E. Aitug, “Increasing driving safety with a multiple vehicle detection and tracking system using ongoing vehicle shadow information,” IEEE Systems Man and Cybernetics (SMC), pp. 3650 - 3656, 10-13 Oct. 2010.
[27] M. F. Jhang and W. J. Liao, “Cooperative and Opportunistic Channel Access for Vehicle to Roadside (V2R) Communications,” Mobile Networks and Applications, Vol. 15, No. 1, pp. 13-19, Feb. 2010.
[28] T. Jiang, Y. Alfadhl, and K. K. Chai, “Efficient Dynamic Scheduling Scheme between Vehicles and Roadside Units Based on IEEE 802.11p/WAVE Communication Standard,”2011 11th International Conference on ITS Telecommunications, pp.120-125, Aug. 2011.