Simulation of Autonomous Vehicles by Using Machine Learning Methods

Document Type : Scientific - Research

Authors
alireza hoveidafard 1
Sina Fard moradinia 2
Babak Golchin 3
Ali ghaffari 4
1 Ph.D. student in Transportation, Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tabriz, Iran
2 Assistant Professor, Department of Civil Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
3 Assistant Professor, Department of Civil Engineering, University of Mohaghegh Ardabili, Ardabil, Iran
4 Associate Professor, Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
10.22119/jte.2023.348840.2609
Abstract
Increasing population and urbanization have been caused many problems such as: increased traffic in cities, travel time, cost, fuel consumption, air pollution and so on. In the mentioned situation, some methods are needed to increase the safety and efficiency of the transportation system. In this regard, one of the programs that can be implemented to improve transportation is the use of Autonomous Vehicles. These vehicles have attracted the attention of researchers and industries, so that many transportation experts are currently working on this field. This system can act as the basis for economic growth and countries development. In the present study, the roles of AVs in transportation system have been discussed. The main structure of this project is neural network as one of the machine learning methods. The task of this section is to learn the system and make decisions based on the current condition.  Accordingly, the combinations of Dense and Convolutional neural networks and Udacity simulator have been used. It showed that the simulator can help to speed up the process and implementation of AVs. The results indicated that there is high similarity between human driving cars and AVs which are implemented by the Udacity simulator.

Keywords

Subjects

  • Abe, R. (2019) “Introducing autonomous buses and taxis: Quantifying the potential benefits in Japanese transportation systems”, Transportation Research Part A: Policy and Practice, 126: 94-113. https://doi.org/10.1016/j.tra.2019.06.003.

 

  • AbdelGawad, H.; Othman, K. (2020) “Multifaceted Synthesis of Autonomous Vehicles’ Emerging Landscape. In Connected and Autonomous Vehicles in Smart Cities”, CRC Press: Boca Raton, FL, USA: 67–113. https://doi.org/1201/9780429329401-3.

 

  • Albini, A., Tokody, D., Rajnai, Z. (2019) “Theoretical study of Cloud Technologies, Interdisciplinary Description of Complex Systems”, . https://doi.org/10.7906/ indecs.17.3.11, Vol. 17, No. 3-A. 511-519.

 

 

 

  • Bajpayee, D. and J. Mathur. A. (2015) “comparative study about autonomous vehicle. in Innovations in Information, Embedded and Communication Systems (ICIIECS) ”, International Conference on. IEEE. https://doi.org/1109/ICIIECS.2015.7193002

 

  • Barabás, I.; Todoru¸t, A.; Cordo¸s, N.; Molea, A. (2017) “Current challenges in autonomous driving”, IOP Conf. Ser. Mater. Sci. Eng,252, 012096. [CrossRef].

 

  • Barzegar, A.; Doukhi, O.; Lee, D.-J. (2021) “Design and Implementation of an Autonomous Electric Vehicle for Self-Driving Control under GNSS-Denied Environments”, Appl. Sci. 2021, 11, 3688. https://doi.org/ 10.3390/app11083688.

 

  • Bertoncello, M. and Wee, D (2015) “Ten ways autonomous driving could redefine the automotive world”, McKinsey & Company.

 

 

  • Botello, B., Buehler, R., Hankey, S., Mondschein A, Jiang Z (2019). “Planning for walking and cycling in an autonomous-vehicle future. Transportation research interdisciplinary perspectives”, 1:100012. https://doi.org/10.1016/j.trip.2019.100012

 

  • Dandi, , Grueber, B., Friese, H., Bogenberger, K. (2019) “Design and Simulation of a Public-Transportation-Complimentary Autonomous Commuter Shuttle”, Transportation Research Procedia. 41: p. 240-250. https://doi.org/10.1016/j.trpro.2019.09.043

 

  • Frey, C.B and Osborne, M.A. (2017) “The future of employment: how susceptible are jobs to computerisation? Technological forecasting and social change”, 114: p. 254-280.

 

  • Friedrich, B. (2016) “The Effect of Autonomous Vehicles on Traffic”, In Autonomous Driving, edited by Barbara Lenz, Markus Mauer, and J Christian Gerdes, 317–34. Berlin: Springer. https://doi.org/10.1007/978-3-662-48847-8_16.

 

  • Goli, M. and Eskandarian, A. (2020) “Merging Strategies, Trajectory Planning and Controls for Platoon of Connected, and Autonomous Vehicles”, International Journal of Intelligent Transportation Systems Research, 18(1): p. 153-173.

 

  • Guerrero-Ibanez, J.A., S. Zeadally, and Contreras-Castillo, J. (2015) “Integration challenges of intelligent transportation systems with connected vehicle, cloud computing, and internet of things technologies”, IEEE Wireless Communications22(6): 122-128. http://doi.org/1109/MWC.2015.7368833.

 

  • Guerrero-Ibáñez, J.; Zeadally, S.; and Contreras-Castillo, J. (2018) “Sensor technologies for intelligent transportation systems”. http://doi.org/ 10.3390/s18041212.

 

  • Gyergyay, B., Chaniotakis, M., Louen, C., Klar, W., Antoniou, C. (2019) “Wider Impacts and Scenarios Evaluation of Autonomous and Connected Transport: the WISE-ACT COSTAction”,Transportation Research Procedia, p. 447 449 .https://doi:1016/j.trpro.2019.09.075

 

  • Hegedűs, T.,Németh, B and Gáspár, P. (2019) “Graph-based multi-vehicle overtaking strategy for autonomous vehicles”, IFAC-PapersOnLine, 52(5): p. 372-377.

 

  • Huang, Y., Wang, H., Khajepour, A., Ding, H., Yuan, K., Qin, Y. (2019). “A Novel Local Motion Planning Framework for Autonomous Vehicles based on resistance network and model predictive control”, IEEE Transactions on Vehicular Technology. PP(99):1-1 https://doi:1109/TVT.2019.2945934.

 

  • Isaac, J.T., Camara, J.S., Zeadally, S., Marquez, J.T. (2008) “A secure vehicle-to-roadside communication payment protocol in vehicular ad hoc networks”. Computer Communications, 2008. 31(10): p. 2478-2484. https://doi.org/10.1016/j.comcom.2008.03.012.

 

  • Jahromi, B.S., Tulabandhula, T., Cetin, S. (2019) “Real-Time Hybrid Multi-Sensor Fusion Framework for Perception in Autonomous Vehicles”, MDPI Sensors 19:4357.

 

  • Jiang, T., Petrovic, S., Ayyer, U., Anand, T., Husain, S. (2015) “Self-driving cars: Disruptive or incremental. Applied Innovation Review”, 1: p. 3- 22.

 

  • Jinyuan, S., Chi, Z and Yuguang, F. (2007). “An ID-based framework achieving privacy and non-repudiation”, In Proceedings of IEEE vehicular ad hoc networks, military communications conference (MILCOM) (pp. 1–7).

 

  • Johnson, C. (2017) “Readiness of the Road Network for Connected and Autonomous Vehicle”, RAC Foundation: London, UK.

 

  • Kim, C., Jin, Y,G., Park, J., Kang, D. (2019) “The influence of an autonomous driving car operation on commuters’ departure times”, Procedia Computer Science, 2019. 151: p. 85-91.

 

  • Köhler, P., Connette, C. and Verl, A. (2013) “Vehicle tracking using ultrasonic sensors & joined particle weighting. in Robotics and Automation (ICRA) ”, IEEE International Conference on. 2013. IEEE. https://doi.org/1109/ICRA.2013.6630979.

 

  • Kyriakidis, M.; Happee, R.; De Winter, J.C.F. (2015) “Public opinion on automated driving: Results of an international questionnaire among 5000 respondents”, Transp. Res. Part F, 32, 127–140. https://doi.org/10.1016/j.trf.2015.04.014.

 

  • Latham, A, Nattrass, M. (2019) “Autonomous vehicles, car-dominated environments, and cycling: Using an ethnography of infrastructure to reflect on the prospects of a new transportation technology”. Journal of Transport Geography. 81:102539. https://doi.org/10.1016/j.jtrangeo.2019.102539

 

  • Litman, T. (2020) “Autonomous vehicle implementation predictions”, Victoria Transport Policy Institute Victoria, Canada.

 

  • Lu, X.L.R. (2015) “VEHICULAR AD HOC NETWORK SECURITY AND PRIVACY”, John Wiley & Sons, Inc., Hoboken, New Jer.

 

  • Maghenem, M., Loria, A., Nuno, E., Panteley, E. (2021). “Distributed full-consensus control of nonholonomic vehicles under non-differentiable measurement delays”, IEEE Control Systems Letters, IEEE, 5 (1),.97-102. https://doi.org/10.1109/lcsys.2020.3000676.

 

  • Malokin, A., Circella, G., Mokhtarian, P.L. (2019). “How do activities conducted while commuting influence mode choice? Using revealed preference models to inform public transportation advantage and autonomous vehicle scenarios”, Transportation Research Part A: Policy and Practice. 124:82-114. https://doi.org/10.1016/j.tra.2018.12.015.

 

  • Medina-Tapia, M., Robusté, F. (2019) “Implementation of Connected and Autonomous Vehicles in Cities Could Have Neutral Effects on the Total Travel Time Costs: Modeling and Analysis for a Circular City”, Sustainability. 11(2):482. https://doi.org/10.3390/su11020482.

 

  • Ongel, A., Loewer, E., Roemer, F., Sethuraman, G., Chang, F., Lienkamp, M. (2019). “Economic assessment of autonomous electric microtransit vehicles”, Sustainability 11 (3), 648. https://doi.org/10.3390/su11030648.

 

  • Offor, P. (2012). “Vehicle ad hoc network (vanet): Safety benefits and security challenges”, Offor, Patrick, Vehicle Ad Hoc Network (VANET): Safety Benefits and Security Challenges. http://dx.doi.org/10.2139/ssrn.2206077.

 

 

  • Pisarov, J., Mester, G. (2021) “The Future of Autonomous Vehicles” , FME Tansactions, Vol. 49, No. 1, 29-35, http://doi.org/. 10.5937/fme2101029P.

 

  • Rajasekhar, M.V., Jaswal, A.K. (2015). “Autonomous vehicles: the future of automobiles” , in Transportation Electrification Conference (ITEC), IEEE International. IEEE. https://doi.org/1109/ITEC-India.2015.7386874.

 

 

 

 

  • Sanguesa, J.A., Torres-Sanz, V., Garrido, P., Martinez, F.J., Marquez-Barja, J.M. ( 2021) “A Review on Electric Vehicles: Technologies and Challenges” , Smart Cities, 4, 372–404. https://doi.org/10.3390/smartcities4010022.

 

 

  • Shaheen, S.A., Finson, R. (2013) “Intelligent transportation systems. Reference Module in Earth Systems and Environmental Sciences” , Elsevier, Intelligent Transportation Systems. https://doi.org/ 10.1016/B978-0-12-409548-9.01108-8, 11-Sep-13(7).

 

  • Stoma, M., Dudziak, A., Caban, J., Drozdziel, P. (2021). “The Future of Autonomous Vehicles in the Opinion of Automotive Market Users” ,Energies, 14, 4777. https://doi.org/10.3390/en14164777.

 

  • Taha, A.E., AbuAli, N. (2018) “Route planning considerations for autonomous vehicles”, IEEE Communications Magazine. 56(10): 78-84. https://doi.org/ 1109/MCOM.2018.1800135.

 

  • Targe, P.A., Satone, M. P. (2016) “Real-Time Intelligent Transportation System based on VANET”, planning. 2: p. 3. e-ISSN: 2395 -0056.

 

  • This is what the evolution of self-driving cars looks like. (2016), Available from: https://www.businessinsider.de/what-are-the-differentlevels-of-driverless-cars-2016- 10/?r=US&IR=T#/#-1.

 

  • Thorn, E., Kimmel, S.C., Chaka, M. (2018) “A framework for automated driving system testable cases and scenarios”, (Report No. DOT HS 812 623). Washington, DC: National Highway Traffic Safety Administration.

 

  • Velasco-Hernandez, G., Yeong, D.J., Barry, J., Walsh, J. (2020) “Autonomous Driving Architectures, Perception and Data Fusion: A Review”, https://doi.org/1109/ICCP51029.2020.9266268.

 

  • Wadud, Z, MacKenzie, D., Leiby, P. (2016). “Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles”, Transportation Research Part A: Policy and Practice. 86:1-18. https://doi.org/10.1016/j.tra.2015.12.001.

 

  • Wiodzimierz, C., Iwona, G. (2019). “Autonomous vehicles in urban agglomerations”, Transportation Research Procedia 40., 655–662, 13th International Scientific Conference on Sustainable, Modern and Safe Transport (TRANSCOM), High Tatras, Novy Smokovec – Grand Hotel Bellevue, Slovak Republic, May 29-31. https://doi.org/10.1016/j.trpro.2019.07.093.

 

 

  • Yudin, D.A., Skrynnik, A., Krishtopic A., Belkin, I., Panov, A.I. (2019) “Object Detection with Deep Neural Networks for Reinforcement Learning in the Task of Autonomous Vehicles Path Planning at the Intersection. Optical Memory and Neural Networks”, 28(4): 283-295. https://doi.org/10.3103/S1060992X19040118.

 

  • Zeadally, S., Hunt, R., Chen, Y.S., Irwin, A. (2012) “Vehicular ad hoc networks (VANETS): status, results, and challenges”, Telecommunication Systems, 2012. 50(4): p. 217-241.

 

  • Zemrane, H., Baddi, Y., Hasbi, A. (2019) “Mobile AdHoc networks for Intelligent Transportation System: Comparative Analysis of the Routing protocols. Procedia Computer Science”, 160: p. 758-765. https://doi.org/10.1016/j.procs.2019.11.014.

 

  • Zontone, P., Affanni, A., Bernardini, R., Del Linz, L., Piras, A., and Rinaldo, R. (2020) “Stress evaluation in simulated autonomous and manual driving through the analysis of skin potential response and electrocardiogram signals”, Sensors 20:2494. doi: 10.3390/s20092494.
Quarterly Journal of Transportation Engineering
Volume 15, Issue 2 - Serial Number 59
Winter 2024
Pages 3483-3507

  • Receive Date 25 June 2022
  • Revise Date 27 December 2022
  • Accept Date 18 April 2023