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Modeling driver behavior in the dilemma zone based on stochastic model predictive control
- Source :
- PLoS ONE, PLoS ONE, Vol 16, Iss 2, p e0247453 (2021)
- Publication Year :
- 2021
- Publisher :
- Public Library of Science (PLoS), 2021.
-
Abstract
- Driver behavior is considered one of the most important factors in the genesis of dilemma zones and the safety of driver-vehicle-environment systems. An accurate driver behavior model can improve the traffic signal control efficiency and decrease traffic accidents in signalized intersections. This paper uses a mathematical modeling method to study driver behavior in a dilemma zone based on stochastic model predictive control (SMPC), along with considering the dynamic characteristics of human cognition and execution, aiming to provide a feasible solution for modeling driver behavior more accurately and potentially improving the understanding of driver-vehicle-environment systems in dilemma zones. This paper explores the modeling framework of driver behavior, including the perception module, decision-making module, and operation module. The perception module is proposed to stimulate the ability to perceive uncertainty and select attention in the dilemma zone. An SMPC-based driver control modeling method is proposed to stimulate decision-making behavior in the dilemma zone. The operation module is proposed to stimulate the execution ability of the driver. Finally, CarSim, the well-known vehicle dynamics analysis software package, is used to verify the proposed models of this paper. The simulation results show that the SMPC-based driver behavior model can effectively and accurately reflect the vehicle motion and dynamics under driving in the dilemma zone.
- Subjects :
- Computer science
Social Sciences
Transportation
02 engineering and technology
CarSim
Motion (physics)
Executive Function
Cognition
0202 electrical engineering, electronic engineering, information engineering
Psychology
Attention
Multidisciplinary
Simulation and Modeling
Physics
05 social sciences
Accidents, Traffic
Classical Mechanics
Transportation Infrastructure
Physical Sciences
Visual Perception
Medicine
Engineering and Technology
Sensory Perception
020201 artificial intelligence & image processing
Research Article
Automobile Driving
Science
Decision Making
Acceleration
Control (management)
Research and Analysis Methods
Civil Engineering
Vehicle dynamics
Traffic signal
Acoustic Signals
0502 economics and business
Humans
Stochastic Processes
Behavior
050210 logistics & transportation
Cognitive Psychology
Biology and Life Sciences
Stochastic model predictive control
Control engineering
Acoustics
Models, Theoretical
Roads
Dilemma
Cognitive Science
Environment Design
Perception
Neuroscience
Subjects
Details
- ISSN :
- 19326203
- Volume :
- 16
- Database :
- OpenAIRE
- Journal :
- PLOS ONE
- Accession number :
- edsair.doi.dedup.....fec40cb67363917b16d9ed3fe50ec74a