سامانه فازی عصبی هشدار تصادف تعاملی مبتنی بر رفتار راننده در تصادفات زنجیره ای با استفاده از ارتباطات بین خودرویی

نوع مقاله : مقاله پژوهشی

نویسنده

گروه کامپیوتر، دانشکده فنی و مهندسی، دانشگاه ملایر، ملایر، همدان

چکیده

عدم توجه به تفاوت رانندگان در میزان درک خطر در سامانه های هشدار تصادف موجب می شود هشدارهای نابجا و غیر ضروری در این سامانه ها افزایش یابد. در این پژوهش مدلی مبتنی بر فناوری ارتباطات بین خودرویی و براساس رفتار راننده ارائه شده است. در مدل پیشنهادی فعال سازی سامانه مبتنی بر شاخص درک خطر انجام می پذیرد. سپس موقعیت هایی که به صورت بالقوه خطرناک محسوب می شوند با استفاده از یک شبکه عصبی شناسایی می گردد. در گام بعد اختلاف شتاب ترمز با فرض هشدار یا عدم هشدار به راننده با استفاده از یک تخمین گر فازی عصبی محاسبه می شود. در نهایت براساس سابقه شتاب ترمز راننده، نسبت به هشداردهی به ایشان اقدام می گردد. نتایج بر روی مجموعه داده های محک ان جی سیم (NGSIM) که شامل بیش از 11 میلیون رکورد از رفتار رانندگی حدود 3300 راننده می باشد، با استفاده از نرم افزار متلب ارزیابی گردیده است. دقت سامانه در شناسایی موقعیت های بالقوه خطر 97 درصد بوده است و خطای تشخیص شتاب ترمز 8/4 درصد می باشد. دقت سامانه درهشدار دهی نیز 98 درصد بدست آمده است . بر این اساس مدل پیشنهادی با حذف هشدارهای غیرضروری و نابجا موجب افزایش اطمینان راننده به سامانه و سفارشی شدن آن بر اساس رفتار رانندگی می گردد.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Neuro-fuzzy cooperative collision warning system in based on driver behavior in chain accident using connected vehicles

نویسنده [English]

  • Hamidreza Eftekhari
Assistant Professor, Department of Computer Engineering, Malayer University, Malayer, Iran.
چکیده [English]

Failure to pay attention to the difference in drivers' perceptions of danger in collision warning systems increases unnecessary warnings. In this research, a model based on connected vehicle technology and based on driver behavior is presented. In the proposed model, the system is activated based on risk perception index. Situations that are potentially dangerous are then identified using a neural network. In the next step, the brake acceleration difference is calculated using an adaptive neural fuzzy estimator between two situations when the driver receives a warning or he does not. Finally, based on the driver's brake history, he will be warned. The results are evaluated on NGSIM benchmark data set, which contains more than 11 million records of driving behavior of about 3300 drivers, using MATLAB software. The accuracy of the system in detecting potential risk situations is 97% .The accuracy of the warning system is 98%. Accordingly, the proposed model increases the driver's confidence and customizes warning system based on driving behavior by eliminating unnecessary warnings.

کلیدواژه‌ها [English]

  • Cooperative Collision Warning System
  • Emergency Electronic Brake Light
  • Adaptive Neuro Fuzzy Inference Systems
  • Connected Vehicles
-Ankrum, D. R., (1992), “Ivhs-smart vehicles, smart roads”, Traffic Safety (Chicago), 92(3).
-Chang, X., Li, H., Qin, L., Rong, J., Lu, Y., & Chen, X., (2019), “Evaluation of cooperative systems on driver behavior in heavy for condition based on a driving simulator’, Accident Analysis & Prevention, 128, pp.197-205.
-Chen, C., Xiang, H., Qiu, T., Wang, C., Zhou, Y., & Chang, V., (2018), “A rear-end collision prediction scheme based on deep learning in the Internet of Vehicles”, Journal of Parallel and Distributed Computing, 117, pp.192-204.
-Evans, L., (1991), “Traffic safety and the driver, Science Serving Society”.
-Fu, Y., Li, C., Luan, T. H., Zhang, Y., & Yu, F. R., (2019), “Graded warning for rear-end collision: An artificial intelligence-aided algorithm”, IEEE Transactions On Intelligent Transportation Systems, 21(2), pp.565-579.
-Garcia-Costa, C., Egea-Lopez, E., Tomas-Gabarron, J. B., Garcia-Haro, J., & Haas, Z. J., (2011),  “A stochastic model for chain collisions of vehicles equipped with vehicular communications”, IEEE Transactions on Intelligent Transportation Systems, 13(2), pp.503-518.
-Hayward, J. C., (1972), “Near miss determination through use of a scale of danger”.
-Huang, J., Chen, Y., Peng, X., Hu, L., & Cao, D., (2020), “Study on the driving style adaptive vehicle longitudinal control strategy”, IEEE/CAA Journal of Automatica Sinica, 7(4), pp.1107-1115.
-Iranmanesh, S. M., Moradi-Pari, E., Fallah, Y. P., Das, S., & Rizwan, M., (2016), “Robustness of cooperative forward collision warning systems to communication uncertainty”, In 2016 Annual IEEE Systems Conference (SysCon), pp. 1-7.
-Jang, J. S., (1993), “ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics”, 23(3), pp.665-685.
-Kondoh, T., Yamamura, T., Kitazaki, S., Kuge, N., & Boer, E. R., (2008), “Identification of visual cues and quantification of drivers' perception of proximity risk to the lead vehicle in car-following situations”, Journal of Mechanical Systems for Transportation and Logistics, 1(2), pp.170-180.
-Kusano, K. D., & Gabler, H. C., (2012), “Safety benefits of forward collision warning, brake assist, and autonomous braking systems in rear-end collisions”, IEEE Transactions on Intelligent Transportation Systems, 13(4), pp.1546-1555.
-Lee, D., & Yeo, H., (2016), “Real-time rear-end collision-warning system using a multilayer perceptron neural network”, IEEE Transactions on Intelligent Transportation Systems, 17(11), pp.3087-3097.
-Lu, G., Cheng, B., Lin, Q., & Wang, Y., (2012), “Quantitative indicator of homeostatic risk perception in car following”, Safety science, 50(9), pp.1898-1905.
-Ma, X., & Andréasson, I., (2006), “Estimation of Driver Reaction Time from Car-Following Data: Application in Evaluation of General Motor–Type Model”, Transportation research record, 1965(1), pp.130-141.
-Milanés, V., Pérez, J., Godoy, J., & Onieva, E., (2012), “A fuzzy aid rear-end collision warning/avoidance system”, Expert Systems with Applications, 39(10), pp.9097-9107.
-Moon, S., Moon, I., & Yi, K., (2009), “Design, tuning, and evaluation of a full-range adaptive cruise control system with collision avoidance”, Control Engineering Practice, 17(4), pp.442-455.
-Murphey, Y. L., Milton, R., & Kiliaris, L., (2009), “Driver's style classification using jerk analysis”, In 2009 IEEE Workshop on Computational Intelligence in Vehicles and Vehicular Systems, pp. 23-28.
-Muslim, H., & Itoh, M., (2021), “Long-term evaluation of drivers’ behavioral adaptation to an adaptive collision avoidance system”, Human factors, 63(7), pp.1295-1315.
-Nadimi, N., Ragland, D. R., & Mohammadian Amiri, A., (2020), “An evaluation of time-to-collision as a surrogate safety measure and a proposal of a new method for its application in safety analysis”, Transportation letters, 12(7), pp.491-500.
-NGSIM, (2021), FHWA., http://ngsim.fhwa.dot.gov.
-Smith, D. L., Najm, W. G., & Glassco, R. A., (2002),  “Feasibility of driver judgment as basis for a crash avoidance database”, Transportation research record, 1784(1), pp.9-16.
-Szczurek, P., Xu, B., Wolfson, O., & Lin, J., (2012), “Estimating relevance for the emergency electronic brake light application”, IEEE Transactions on Intelligent Transportation Systems, 13(4), pp.1638-1656.
-Tan, H., Lu, G., & Liu, M., (2021), “Risk Field Model of Driving and Its Application in Modeling Car-Following Behavior”, IEEE Transactions on Intelligent Transportation Systems.
-Traffic Safety Facts A Compilation of Motor Vehicle Crash Data (Annual Report) ,(2018), “National Highway Traffic Safety Administration (NHTSA)”.
-Tsai, M. F., Chao, Y. C., Chen, L. W., Chilamkurti, N., & Rho, S., (2015), “Cooperative emergency braking warning system in vehicular networks”, EURASIP Journal on Wireless Communications and Networking, pp.1-14.
 
-Vogel, K., (2003), “A comparison of headway and time to collision as safety indicators”, Accident analysis & prevention, 35(3), pp.427-433.
-Wang, J., Yu, C., Li, S. E., & Wang, L., (2015), “A forward collision warning algorithm with adaptation to driver behaviors”, IEEE Transactions on Intelligent Transportation Systems, 17(4), pp.1157-1167.
-Wang, P., Wu, W., Deng, X., Xiao, L., Wang, L., & Li, M., (2017), “Novel cooperative collision avoidance model for connected vehicles”, Transportation Research Record, 2645(1), pp.144-156.
-Wang, S. Y., Cheng, Y. W., Lin, C. C., Hong, W. J., & He, T. W., (2008), “A vehicle collision warning system employing vehicle-to-infrastructure communications”, In 2008 IEEE Wireless Communications and Networking Conference, pp. 3075-3080.
-World Health Organization, (2018), “Global status report on road safety 2018: Summary (No. WHO/NMH/NVI/18.20)”, World Health Organization.
-Xie, G., Gao, H., Qian, L., Huang, B., Li, K., & Wang, J., (2017), “Vehicle trajectory prediction by integrating physics-and maneuver-based approaches using interactive multiple models”, IEEE Transactions on Industrial Electronics, 65(7), pp.5999-6008.
-Zhang, Y., Wu, C., Qiao, C., & Hou, Y., (2019), “The effects of warning characteristics on driver behavior in connected vehicles systems with missed warnings”, Accident Analysis & Prevention, 124, pp.138-145.