Volume 19, Issue 1 (2019)                   Modares Mechanical Engineering 2019, 19(1): 63-74 | Back to browse issues page

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Mozaffari H, Nahvi A. Modeling of Driver Behavior in Complicated Traffic Conditions by Combining Psychological Theories and Automotive Control Techniques. Modares Mechanical Engineering. 2019; 19 (1) :63-74
URL: http://journals.modares.ac.ir/article-15-23041-en.html
1- Dynamics & Pulsation Control Department, Mechanical Engineering Faculty, K.N. Toosi University of Technology, Tehran, Iran , mozaffari.ha@email.kntu.ac.ir
2- Mechanical Engineering Department, Mechanical Engineering Faculty, K.N. Toosi University of Technology, Tehran, Iran
Abstract:   (2263 Views)
Regarding the growing development of traffic perception systems, advanced driver assistance systems play a significant role in improving automotive safety. They should be able to guide intelligent vehicles through complicated driving scenarios. The complex nature of the driving process results in complicated control engineering methods. Modeling driver behavior based on psychological concepts would simplify the driving logic and human-machine interaction. In this research, psychological concepts and tire force limitations are formulated based on vehicle kinematics and kinetics as a function of speed and curvature. A multi-objective cost function is defined based on psychological concepts and tire force limits. The speed and the curvature, at which the cost function is minimal, are selected as the decided values. Saturated proportional controllers set the vehicle speed and path curvature on the decided values by adjusting the steering angle of the front wheels, accelerator pedal position, and brake force. The model performance is evaluated by a complicated driving scenario, which includes travelling in the same and opposite directions, presence of obstacles with different sizes and speeds, and high curvature paths. The model avoids face-to-face collisions with a time-to-collision close to 0.72 s. Also, it can avoid obstacles in tight spaces as narrow as 30 cm. Simulation results indicate that the proposed driver model performs safely at the presence of moving obstacles and tight spaces.
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Received: 2018/07/13 | Accepted: 2018/08/29 | Published: 2019/01/1

1. 1- Nagarajan K. Design of self-learning fuzzy based driver model. SAE Technical Paper. 2017;2017-26-0081. [Link]
2. Sazgar H, Azadi S, Kazemi R. Trajectory planning and integrated control with the Nonlinear Bicycle Model for high-speed autonomous lane change. Modares Mechanical Engineering. 2018;18(2):103-114. [Persian] [Link]
3. Chonga L, Abbas MM, Flintschc AM, Higgs B. A rule-based neural network approach to model driver naturalistic behavior in traffic. Transportation Research Part C: Emerging Technologies. 2013;32:207-223. [Link] [DOI:10.1016/j.trc.2012.09.011]
4. Naseralavi SS, Saffarzadeh M, Nadimi N, Mamdoohi AR. Applying time-to-collision to enhance the coefficient of determination for GHR Car-Following Model in Deceleration Mode. Modares Civil Engineering Journal. 2011;11:17-26. [Persian] [Link]
5. Gaspar P, Szabo Z, Bokor J, Nemeth B. Robust control design for active driver assistance systems: A linear-parameter-varying approach. Basel: Springer; 2017. [Link] [DOI:10.1007/978-3-319-46126-7]
6. Receveur JB, Victor S, Melchior p. Multi-criteria trajectory optimization for autonomous vehicles. IFAC-PapersOnLine. 2017;50(1):12520-12525. [Link] [DOI:10.1016/j.ifacol.2017.08.2063]
7. Hayashi R, Isogai J, Raksincharoensak P, Nagai M. Autonomous collision avoidance system by combined control of steering and braking using geometrically optimized vehicular trajectory. Vehicle System Dynamics, International Journal of Vehicle Mechanics and Mobility. 2012;50(Supple 1):151-168. [Link]
8. Cadkhodajafarian A, Analooee A, Azadi S, Kazemi R. Collision-free navigation and control for autonomous vehicle in complex urban environments. Modares Mechanical Engineering. 2018;17(11):277-288. [Persian] [Link]
9. Rasekhipour Y, Khajepour A, Chen SK, Litkouhi B. A potential field-based model predictive path-planning controller for autonomous road vehicles. IEEE Transactions on Intelligent Transportation Systems. 2017;18(5):1255-1267. [Link] [DOI:10.1109/TITS.2016.2604240]
10. Fuller R. Towards a general theory of driver behaviour. Accident Analysis & Prevention. 2005;37(3):461-472. [Link] [DOI:10.1016/j.aap.2004.11.003]
11. Wilde GJS. The theory of risk homeostasis: Implications for safety and health. 1982;2(4):209-226. [Link]
12. Gibson JJ, Crooks LE. A theoretical field-analysis of automobile-driving. The American Journal of Psychology. 1938;51(3):453-471. [Link] [DOI:10.2307/1416145]
13. Hollnagel E. Time and time again. Theoretical Issues in Ergonomics Science. 2002;3(2):143-158. [Link] [DOI:10.1080/14639220210124111]
14. Mozaffari H, Nahvi A. A Motivational driver steering model: Task difficulty homeostasis from control theory perspective. Cognitive Systems Research. 2018;50:67-82. [Link] [DOI:10.1016/j.cogsys.2018.03.007]
15. Pacejka HB, Bakker E. The magic formula tire model. Vehicle System Dynamics, International Journal of Vehicle Mechanics and Mobility. 1992;21(Suppl 001):1-18. [Link]
16. Mehrabi N, Nahvi A, Azari Sh. Simulation of vehicle roll control in a driving simulator [Dissertation]. Tehran: K. N.Toosi University of Technology; 2010. [Persian] [Link]

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