تدوین راهبرد فناوری‌های خودروی خودران با رویکرد ماتریس جذابیت - توانمندی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دکتری مدیریت تکنولوژی، دانشکده مدیریت و اقتصاد، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران.

2 استاد گروه مدیریت صنعتی، دانشکده مدیریت و اقتصاد، دانشگاه تربیت مدرس، تهران.

3 استاد مجتمع دانشگاهی مدیریت و مهندسی صنایع، دانشگاه صنعتی مالک اشتر، تهران.

چکیده

رانندگان از دلایل اصلی تصادفات در خودروهای کنونی هستند که می تواند ناشی از محدودیت های انسانی مانند بینایی، خستگی و حواس پرتی باشد. یکی از راه حل‌های کاهش این حوادث، حذف راننده از رانندگی است. بنابراین فناوری خودروی خودران یا بدون راننده در سالهای اخیر در حال طراحی و توسعه است. با توجه به نیاز فناوری خودروی خودران مانند هر فناوری پیشرفته دیگر به زیرساخت فناورانه، اجتماعی، اقتصادی و مدیریتی، لازم است از الان این آمادگی فراهم گردد. هدف از این تحقیق، تدوین راهبرد فناوری برای فناوری های کلیدی آینده خودروی خودران بود. روش تحقیق به این صورت بود که بعد از شناسایی فناوری های کلیدی خودروی خودران، و تشکیل ماتریس جذابیت و ماتریس توانمندی برای این فناوری ها، تدوین راهبرد فناوری برای آنها انجام شد. نتیجه تحقیق این بود که برای 40 مورد از فناوری های خودروی خودران، تدوین راهبرد فناوری انجام گردید. با توجه به نواحی قرارگیری این 40 فناوری در چهار بخش مختلف نمودار تدوین راهبرد فناوری، تجزیه و تحلیل متفاوت برای آنها صورت گرفت. در ناحیه اول این نمودار، تعداد 8 فناوری با توانمندی بالا و جذابیت بالا، و در ناحیه دوم این نمودار، تعداد 26 فناوری با توانمندی بالا و جذابیت پایین، و در ناحیه چهارم، تعداد 6 فناوری با توانمندی پایین و جذابیت پایین قرار گرفتند. ضمنا در فرآیند تحقیق، نتایج فرعی دیگری نیز مانند: اهداف کلان و فناوری های کلیدی خودروی خودران، ماتریس جذابیت و ماتریس توانمندی فناوری کسب گردید که این نتایج فرعی نیز تجزیه و تحلیل شد.
 

کلیدواژه‌ها

عنوان مقاله [English]

Implementation of Technology Strategy for Autonomous Vehicle Technologies by Attractiveness and Capability Matrix Approach

نویسندگان [English]

  • Hamid Hanifi 1
  • Adel Azar 2
  • Manouchehr Manteghi 3
1 Department of Technology Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran.
2 Department of Industrial Management, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran.
3 University of Industrial Management and Engineering Complex, Malek Ashtar University of Technology, Tehran, Iran
چکیده [English]

One of the main causes of accidents in current cars are drivers, which can be caused by human limitations such as vision, fatigue and distraction. One of the ways to reduce these accidents is to remove the driver from driving. Another challenge of cars is the issue of pollution. For example, more than 90% of carbon monoxide gas is one of the most important air pollutants produced by motor vehicles in Tehran. Therefore, the autonomous vehicle or driverless car is being designed and developed in recent years in developed countries, which can reduce accidents and reduce pollution. Considering the need of autonomous vehicle like any other advanced technologies for technological, social, economic and managerial infrastructures, it is necessary to provide this preparation from now on. The purpose of this research was to compile a technology strategy for the key technologies of autonomous vehicle. The research method was such that after identifying the key technologies of the autonomous vehicle, and forming the attractiveness matrix and capability matrix for these technologies, a technology strategy was prepared for them. The result of the research was that a technology strategy was prepared for 40 technologies. According to the placement areas of these technologies in four different sections of the diagram, different analysis was done for them. For example, in the first area of this diagram, it was determined that 8 out of 40 autonomous vehicle technologies will have high capability and high attractiveness. Their names are as follows: artificial intelligence technology, car cognitive internet technology, fifth generation internet technology for connected autonomous vehicles, internet of things technology in the automotive industry, Blockchain technology platform, technologies related to electric cars, improving the advanced driver assistance system and navigation control module technology. In the second area of this diagram, there are 26 technologies with high capability and low attractiveness, and in the fourth area, there are 6 technologies with low capability and low attractiveness. Also, in the research process, in addition to the main result of the research, other sub results were also obtained. The main objectives autonomous vehicles, the identified technologies of the autonomous vehicle, the technology attractiveness matrix and also the capability matrix were among the sub results of this research. The results of this research can be useful for preparing organizations related to autonomous vehicle technology in the future for development in Iran or even for importing autonomous vehicle technology from industrialized countries.
 
 

کلیدواژه‌ها [English]

  • Technology Strategy
  • Autonomous vehicle
  • Technology Foresight
  • Driverless Car
  • Connected Smart Car

مراجع

[1] HosseinZadeh M., Mehregan, M.R., & Kiani, M. (2013). Operations Research: Science or Technology? Why is it important?, Journal of Science & Technology Policy, Volume 5, Number 4, Summer .{In Persian}.
[2] Safdari Ranjbar, M., Rahmanseresht, H., Manteghi, M., & Ghazinoori, S. (2018). Sectoral Innovation System of a Complex Product System Industry: Gas Turbine. Journal of Science & Technology Policy, 9(4), 55-70. DOI: 10.22034/jstp.2018.9.4.538371. {In Persian}.
[3] Radfar, Reza., Khamseh, Abbas., Madani, Hesameddin.(2009), Technology Commercialization as the Effective Function in Technology and Economy Development, Roshd -e- Fanavari, 20(5),1-10.
[4] Singh, S., (2015). Critical Reasons for Crashes Investigated in the National Motor Vehicle Crash Causation Survey. (traffic safety facts crash•Stats). Washington, DC: national highway traffic safety administration. 13th International IEEE Conference on Intelligent Transportation Systems. Pp. 2–3.
[5] NHTSA(2015). Traffic Safety Facts 2015: A Compilation of Motor Vehicle Crash Data Fatality Analysis Reporting System and the General Estimates System, Department of Transportation, National Highway Traffic Safety Administration, Washington, DC, USA, Tech. Rep. DOT HS 812 384.
[6] Maddah, Mehdi., Rezaei, Alireza., and Mirfallah Nasiri, Nematullah., Investigation of Statistics and Accidents and  Road Fatalities, Research Institute of Statistics, March 2018, Report code: EC-4-4-2-98-06. 2018. {In Persian}.
[7] C. You, J. Lu, and P. Tsiotras, Nonlinear Driver Parameter Estimation and Driver Steering Behavior Analysis for ADAS Using Field Test Data, IEEE Transactions on Human-Machine Systems, vol. 47, no. 5, pp. 686–699, 2017.
[8] Saeb K, Maryam M, Saeed K. Air Pollution Estimation From Traffic Flows in Tehran Highways. Current World Environ 2017 Jun 23;7(1). {In Persian}.
[9] Kakouei A, Vatani A, Idris AK. An Estimation of Traffic Related CO 2 Emissions from Motor Vehicles in the Capital City of, Iran. Iranian journal of environmental health science & engineering. 2012 Dec;9(1):13. {In Persian}.
[10] Bidova, S., and Hassanzadeh, M. (2017). Environmental Investigation of the Automotive Industry and Ways to Reduce its Pollution. Environmental Research and Technology, 3(4), 1-7. https://www.sid.ir/fa/journal/ViewPaper.aspx?id=441873. {In Persian}.
[11] Transportation and Energy Information of the Country, Research Institute of Basic Applied Sciences of Jahad Academic, 1390. {In Persian}.
[12] Skeete, J., (2018). Level 5 Autonomy: the New Face of Disruption in Road Transport. Technological Forecasting & Social Change 134, 22–34 (September).
[13] LaFrance, A.(2018). Self-driving Cars Could Save 300,000 Lives Per Decade in America. The Atlantichttps://www.theatlantic.com/technology/archive/2015/09/self-drivingcars-could-save-300000-lives-per-decade-in-america/407956/, Accessed date: 17.July 2018.
[14] Pettigrew, S., (2017). Why Public Health Should Embrace the Autonomous Car. Aust. N. Z. J. Public Health 41 (1), 5–7.
[15] Fuel Consumption Guide for Iranian light Vehicles, Fuel consumption optimization company, energy optimization management in the transportation sector, standard affairs, compilation of fuel consumption criteria and tables, 2015. {In Persian}.
[16] Transportation and Energy Information of the Country, Research Institute of Basic Applied Sciences of Jahad Academic, 1390. {In Persian}.
[17] Aarabi, M.,(2018). Technology Strategy, Mahkame publication, Tehran, Iran .{In Persian}.
[18] Khalil, T. M.,(2000). Management of Technology: The Key to Competitiveness and Wealth Creation, McGraw Hill.
[19] Danila, N., “Strategic Evaluation and Selection of R&D Project” R&D Management, Vol. 19, No. 1, (1989).
[20] Gora.P, Rub. I,(2007). Traffic Models for Self-driving Connected Cars, Transportation Research Procedia, 14 (2016) 2207–2216. Doi: 10.1016/j.trpro.2016.05.236.
[21] Ahn Jinsoo., Kim, Young Yong., and Kim, Ronny Yongho.,(2018). A Novel WLAN Vehicle-To-Anything (V2X) Channel Access Scheme for IEEE 802.11p-Based Next-Generation Connected Car Networks, Appl. Sci. 2018, 8, 2112; doi:10.3390/app8112112.
 [22] Madeleine de Cock Buning, Lucky Belder & Roeland W. de Bruin, Working paper(2016). Mapping the Legal Framework for the Introduction into Society of Robots as Autonomous Intelligent Systems, at p. 3-4, available on the Internet at http://www.caaai .eu/wp-content/uploads/2012/08/Mapping-L_N-fw-for-AIS.pdf (last accessed on 28 January 2016).
[23] Clarck, K., et al., (1995). Technology Strategy in UK firms, Technology Analysis & Strategic Management, (1995).
[24] Lonita, Silviu., (2017). Autonomous Vehicles: from Paradigms to Technology, IOP Conf. Series: Materials Science and Engineering 252 (2017) 012098 doi:10.1088/1757-899X/252/1/012098.
[25] Chai, Linguo., Cai, Baigen., ShangGuan, Wei., Wang, Jian., and Wang, Huashen.,(2017). Basic Simulation Environment for Highly Customized Connected and Autonomous Vehicle Kinematic Scenarios, Sensors 2017, 17, 1938; doi:10.3390/s17091938.
[26] Reebadiya, Dakshita., Rathod, Tejal., Gupta, Rajesh., Tanwar, Sudeep., and Kumar, Neeraj.,(2021). Blockchain-based Secure and Intelligent Sensing Scheme for Autonomous Vehicles Activity Tracking Beyond 5G Networks, © Springer Science+Business Media, LLC, part of Springer Nature 2021, https://doi.org/10.1007/s12083-021-01073-x.
[27] Alam, Furgan., Mehmood, Rashid., and Katib, Iyad., D2TFRS., (2018). An Object Recognition Method for Autonomous Vehicles Based on RGB and Spatial Values of Pixels, © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018, R. Mehmood et al. (Eds.): SCITA 2017, LNICST 224, pp. 155–168, 2018. https://doi.org/10.1007/978-3-319-94180-6_16.
[28] K.-H.N. Bui, J.J. Jung,(2017). Internet of Agents Framework for Connected Vehicles: A case study on Distributed Traffic Control System, J. Parallel Distrib. Comput. (2017),  https://doi.org/10.1016/j.jpdc.2017.10.019.
[29] Wang, Wei., Zhou, Feng., Li, Wayne.,(2018). Designing the Product- Service System for Autonomous Vehicles, Published by the IEEE Computer Society 1520-9202/18/$33.00 _2018 IEEE.
[30] Melanie Swan. (2015). Connected Car: Quantified Self Becomes Quantified Car. J. Sens. Actuat. Netw. 4, 1 (Feb. 2015), 2–29.
[31] Sibel Yenikaya, Gokhan Yenikaya, and Ekrem Duven. (2013). Keeping the Vehicle on the Road a Survey on on-Road Lane Detection Systems. Comput. Surv. 46, 1 (Oct. 2013).
[32] Linas Baltrunas, Marius Kaminskas, Bernd Ludwig, Omar Moling, Francesco Ricci, Aykan Aydin, Karl- Heinz Luke, and Roland Schwaiger. (2011). InCarMusic: Context-Aware Music Recommendations in a Car. EC-Web 11 (2011), 89–100.
[33] T. Luan, R. Lu, X. Shen, and F. Bai. (2015). Social on the Road: Enabling Secure and Efficient Social Networking on Highways. IEEE Wireless Commun. 22, 1 (Feb. 2015), 44–51.
 [34] Chunhua Liu, K. T. Chau, Diyun Wu, and Shuang Gao. (2013). Opportunities and Challenges of Vehicle-to Home, Vehicle-to-Vehicle, and Vehicle-to-Grid Technologies. Proc. IEEE 101, 11 (Nov. 2013), 2409–2427.
 [35] Lee Boon-Leng, Lee Dae-Seok, and Lee Boon-Giin. (2015). Mobile - Based Wearable-Type of Driver Fatigue Detection by GSR and EMG. In TENCON 2015–2015 IEEE Region 10 Conference. 1–4.
[36] Quartsoft. (2013). How Honda’s V2V And V2P Technology Uses Smartphones to Save Lives? Retrieved Sep 2013 from http://quartsoft.com/blog/201309/honda-v2v-v2p-technology-smartphones#sthash.
[37] Chandra Saha, G., Islam, N.,(1998). Technological Information for Technology Strategy Management, in international Journal of the Computer, the Internet and Managent, vol.6, No3,(1998).
[38] Sbati, Hossein., Karimian, Amir Hoshang., and Aghapour, Hamid.(2016). Getting to know the Basics and Patterns of Technology Strategy Implementation along with a Case Study in Three Industrial Fields, Publication: Tehran, Aindeh Pajoh, (In Persian).

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