Volume 19, Issue 1 (January 2019)
Modares Mechanical Engineering 2019, 19(1): 63-74 |
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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://mme.modares.ac.ir/article-15-23041-en.html
URL: http://mme.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
2- Mechanical Engineering Department, Mechanical Engineering Faculty, K.N. Toosi University of Technology, Tehran, Iran
Abstract: (8773 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.
Article Type: Original Research |
Subject:
Control
Received: 2018/07/13 | Accepted: 2018/08/29 | Published: 2019/01/1
Received: 2018/07/13 | Accepted: 2018/08/29 | Published: 2019/01/1
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