پیوندهای مفید
آمار
| تعداد نشریات | 8 |
| تعداد شماره‌ها | 428 |
| تعداد مقالات | 5,580 |
| تعداد مشاهده مقاله | 6,876,152 |
| تعداد دریافت فایل اصل مقاله | 5,845,896 |
طراحی مسیر مانور تعویض خط در وضعیت اضطراری، مبتنی بر عملکرد راننده ماهر | ||
| نشریه مهندسی مکانیک امیرکبیر | ||
| مقاله 1، دوره 54، شماره 2، اردیبهشت 1401، صفحه 269-288 اصل مقاله (4.63 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22060/mej.2021.20198.7191 | ||
| نویسندگان | ||
| سمیر نیسی مینایی؛ علی غفاری* | ||
| دانشکده مهندسی مکانیک، آزمایشگاه سیستم های پیشرفته کنترلی خودرو، دانشگاه صنعتی خواجه نصیر الدین طوسی، تهران، ایران. | ||
| چکیده | ||
| تعویض خط در سرعتهای بالا، یک مانور راهبردی جهت اجتناب از برخورد است. در این شرایط زمان امکان بروز حادثه عمدتاً کمتر از s2 است؛ لذا یافتن مسیر مناسب و کنترل خودرو با کمترین هزینه زمانی از اهمیت بالایی برخوردار است. در این مقاله، براساس عملکرد راننده ماهر در شرایط مشابه، مسیر مناسب و سیستم کنترلی متناسب با آن، با هدف پایداری خودرو و اجتناب از برخورد طراحی شده است. برای این منظور، با شبیهسازی عملکرد راننده ماهر بوسیله مدل دینامیکی 7 درجه آزادی خودرو، مسیرهای احتمالی شناسایی و با آموزش یک شبکه عصبی، مسیر نهایی انتخاب شده است. جهت هدایت خودرو از یک کنترل‌کننده ترکیبی، شامل: معادلات رفتاری راننده با یک سیستم شبکه عصبی پوششی و دو کنترل‌کننده تناسبی-مشتقی استفاده شده است. مهم‌ترین نوآوری‌های این روش، طراحی هم‌زمان مسیرهای پایدار با حفظ قید هندسی عدم برخورد و هزینه محاسباتی و زمانی ناچیز سیستم کنترلی و طراحی مسیر برای شرایط اضطراری است. نتایج حاصل از شبیه‌سازی‌ها نشان میدهد بیشترین خطای شبکه عصبی طراحی مسیر حدود 11 درصد است. همچنین سیستم کنترلی توانسته در شرایط مختلف سرعتی و اصطکاک جادهای، با حداکثر خطای جابهجایی عرضی cm40 خودرو را هدایت و مسیر را دنبال کند. | ||
| کلیدواژه‌ها | ||
| اجتناب از برخورد؛ راننده ماهر؛ تعویض خط اضطراری؛ خودروی خودران؛ طراحی مسیر | ||
| عنوان مقاله [English] | ||
| Lane Change Path Planning in Emergency Situation Based on Skilled Driver's Performance | ||
| نویسندگان [English] | ||
| Samir Neisy Minaee؛ Ali Ghaffari | ||
| PhD Student, Department of Mechanical Engineering, Advanced Vehicle Control System Laboratory (avcs-lab), Faculty of Mechanical Engineering, K.N. Toosi University of Ttechnology, Tehran, Iran, | ||
| چکیده [English] | ||
| Lane Change at high speeds is a strategic maneuver to avoid the collision. In this situation, The time of an accident possibility is mainly less than two seconds. therefore, finding the proper trajectory and control of the vehicle is crucial at the lowest cost. In this paper, based on a skilled driver’s performance in a similar situation, a proper path and corresponding appropriate control system was designed aiming at vehicle stability preserving and collision avoidance. To this purpose, possible paths were simulated and identified using the vehicle's seven degrees of freedom model and applying the driver’s behavior. A neural network system was trained; then, the trajectory was chosen. A hybrid controller was hired for the vehicle navigation, consisting of the driver's performance pattern with a covering neural network and two proportional derivative controllers. The Novelties of this method are the simultaneous design of a stable path while maintaining geometric constraints and the low computational burden of the path planning and control system. The results show that the highest neural network error is about 11%. Also, the control system has been able to steer the vehicle and follow the trajectory with 40cm maximum lateral displacement error in the speed and road friction different conditions. | ||
| کلیدواژه‌ها [English] | ||
| Autonomous vehicle, Collision avoidance, Skilled driver, Emergency Lane change, Path planning | ||
| مراجع | ||
|
[1] A.E. Wester, K.B.E. Bocker, E.R. Volkerts, J.C. Verster, J.L. Kenemans, Event-related potentials and secondary task performance during simulated driving, Accident Analysis & Prevention, 40(1) (2008) 1-7. [2] M. Zhu, H. Chen, G. Xiong, A model predictive speed tracking control approach for autonomous ground vehicles, Mechanical Systems and Signal Processing, 87 (2017) 138-152. [3] J.B. Receveur, S. Victor, P. Melchior, Autonomous car decision making and trajectory tracking based on genetic algorithms and fractional potential fields, Intel Serv Robotics, 13 (2020) 315–330. [4] S. Yang, Y. Cao, Z. Peng, G. Wen, K. Guo, Distributed formation control of nonholonomic autonomous vehicle via RBF neural network, Mechanical Systems and Signal Processing, 87 (2017) 81-95. [5] H. Zhang, G. Zhang, J. Wang, Observer design for LPV systems with uncertain measurements on scheduling variables: application to an electric ground vehicle, IEEE/ASME Transactions on Mechatronics, 21(3) (2016) 1659-1670. [6] J.A. Decker, S.H. Haus, R. Sherony, Potential benefits of animal-detecting automatic emergency braking systems based on U.S. driving data, Journal of the Transportation Research Board, 2675(10) (2021) 678-688. [7] IIHS HLDI, Collisions with fixed objects and animals. Insurance Institute for Highway Safety, Fatality Facts, (2019). https://www.iihs.org/topics/fatality-statistics/detail/collisions-with-fixed-objects-and-animals [8] M. Fausten, A. Vorentwicklung, accident avoidance by evasive manoeuvres, Chassis Systems Control Robert Bosch GmbH, 2010. [9] S. Sadeghi Namaghi, M. Moavenian, An adaptive modified fuzzy-sliding mode longitudinal control design and simulation for vehicles equipped with ABS system, International Journal of Automotive Engineering, 9(1) (2019) 2895-2907. [10] J. Lenarda, A. Badea Romero, R. Danton, Typical pedestrian accident scenarios for the development of autonomous emergency braking test protocols, Accident Analysis and Prevention, 73 (2014) 73–80. [11] https://www.toyotaofnorthcharlotte.com/blog/so-what-exactly-is-the-moose-test/ [12] J. Nilsson, M. Brannstrom, E. Coelingh, J. Fredriksson, Lane change maneuvers for automated vehicles, IEEE Transactions on Intelligent Transportation Systems, 18(5) (2017) 1087-1096. [13] H. Guo, D. Cao, H. Chen, Z. Sun, Y. Hu, Model predictive path following control for autonomous cars considering a measurable disturbance: Implementation, testing, and verification, Mechanical Systems and Signal Processing, 118 (2019) 41-60. [14] S. Samiee, S. Azadi, R. Kazemi, A. Eichberger, B. Rogic, M. Semmer, Performance evaluation of a novel vehicle collision avoidance lane change algorithm, Advanced Microsystems for Automotive Applications 2015, (2016) 103-116. [15] L. Chen, D. Qin, X. Xu, Y. Cai, J. Xie, A path and velocity planning method for lane changing collision avoidance of intelligent vehicle based on cubic 3-D Bezier curve, Advances in Engineering Software, 132 (2019) 65-73. [16] Y. Choi, K. Lim, J. Kim, Lane change and path planning of autonomous vehicles using GIS, 12th International Conference on Ubiquitous Robots and Ambient Intelligence, KINTEX, Goyang, Korea, (2015) 163-166. [17] A. Norouzi, R. Kazemi, O.R. Abbassi, Path planning and re-planning of lane change manoeuvres in dynamic traffic environments, Int. J. Vehicle Autonomous Systems, 14(3) (2019) 239-264. [18] G. Geng, Z. Wu, H. Jiang, L. Sun, C. Duan, Study on path planning method for imitating the lane changing operation of excellent drivers, Applied Sciences, 8(5) (2018) 1-19. [19] Y. Ding, W. Zhuang, L. Wang, J. Liu, L. Guvenc, Z. Li, Safe and optimal lane-change path planning for automated driving, Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 235(4) (2020) 1070-1083. [20] G. Han, W. Fu, W. Wang, Z. Wu, The lateral tracking control for the intelligent vehicle based on adaptive PID neural network, Sensors, 17(6) (2017)1244. [21] A. Norouzi, R. Kazemi, S. Azadi, Vehicle lateral control in the presence of uncertainty for lane change maneuver using adaptive sliding mode control with fuzzy boundary layer, Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 232(1) (2018) 12-28. [22] H. Sazgar, S. Azadi,R. Kazemi, Trajectory planning and combined control design for critical high-speed lane change manoeuvres. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 234(2-3) (2020) 823-839. [23] Y. Zhong, L. Guo, Y. Zhang, Q. Liu, H. Chen, Optimal lane change control of intelligent vehicle based on MPC, IEEE 2019 Chinese Control And Decision Conference (CCDC), (2019) 1468-1473. [24] J. Wang, H. Zheng, C. Zong, Longitudinal and lateral dynamics control of automatic lane change system, Transactions of the Institute of Measurement and Control, 41(15) (2019) 4322-4338. [25] H. Sazgar, S. Azadi, R. Kazemi, A. Khalaji. Integrated longitudinal and lateral guidance of vehicles in critical high speed manoeuvres, Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics; 233(4) (2019) 994-1013. [26] R. Rajamani, Vehicle dynamics and control, 4 ed., Springer, New York, 2012. [27] Z. Zhang, J. Li, W. Guo, Combined simulation of heavy truck stability under sudden and discontinuous direction change of crosswind with computational fluid dynamics and multi-body system vehicle dynamics software, Advances in Mechanical Engineering, 10(7) (2018) 1–10. [28] E. Velenis, P. Tsiotras, J. Lu, Optimality properties and driver input parameterization for trail-braking cornering, European Journal of Control, 14(4) (2008) 308-320. [29] ISO 3888-2:2011, Passenger cars - test track for a severe lane-change manoeuvre - Part 2: Obstacle avoidance, 2016. https://www.iso.org/standard/57253.html. [30] www.km77.com. [31] S. Gim, S. Lee, Life fellow, safe and efficient lane change maneuver for obstacle avoidance inspired From human driving pattern, IEEE Transactions on Intelligent Transportation Systems, (2020) 1-19. [32] M.I. Razzak, S. Naz, A. Zaib, Deep learning for medical image processing: overview, challenges and the future, Classification in BioApps, 26 (2017) 323-350. [33] H. Lassoued, R. Ketata, S. Yacoub, ECG decision support system based on feedforward neural networks, International Journal on Smart Sensing and Intelligent System, 11(1) (2018) 1-15. [34] A.N. Sharkawy. Principle of neural network and its main types, Review. Journal of Advances in Applied & Computational Mathematics, Avanti Publishers, 7 (2020) 8-19. [35] J.F. Bonnefon, A. Shariff, I. Rahwan, The social dilemma of autonomous vehicles. Science 352, (2016) 1573–1576. [36] Y.E. Bigman, K. Gray, Life and death decisions of autonomous vehicles, J.Nature 579, (2020). [37] E. Awad, S. Dsouza, R. Kim, J. Schulz, J. Henrich, A. Shariff, The moral machine experiment. J.Nature 563, (2018) 59–64. [38]https://www.ted.com/talks/iyad_rahwan_what_moral_decisions_should_driverless_cars_make?language=en#t-310886. | ||
|
آمار تعداد مشاهده مقاله: 690 تعداد دریافت فایل اصل مقاله: 874 |
||