A holistic approach for modeling and verification of human driver behavior

Two-lane rural roadways generate a disproportionate number of fatal and serious injury crashes. Many safety measures to date have reduced crash severity. As roadway design guides have evolved, they have incorporated changes that address safety needs. Advanced vehicle designs have incorporated features that protect occupants. Regulations have addressed risky driver behavior. Technological improvements have reduced emergency response times and transport times to trauma centers. To mitigate rural roadway departure issues, the engineering, education, enforcement, and emergency response (4E) approach has recently been implemented by some highway transportation agencies. Including nonengineering mitigation measures along with engineering mitigations, such as improved roadway and roadside design, and maintenance practices has had positive effects on rural roadway departure crashes. Improved vehicle designs have had positive effects on severity. Improved emergency response has had positive effects on survival rate. Education and enforcement efforts have had positive effects on driver behavior. The Moving Ahead for Progress in the 21st Century Act emphasizes an application of the 4E approach with full flexibility in the use of Highway Safety Improvement Program and Highway Safety Program funds. This paper advances a holistic analytical approach by coordinating the use of the 4Es and identifies the need for research to support and validate the proposed optimization approach. A data-driven model is presented for optimizing the allocation of 4E resources at the program level to address rural roadway departure crash issues. The same model can be applied at the project level.

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Online Parameter Estimation for Human Driver Behavior Prediction

2020 American Control Conference (ACC) ◽

10.23919/acc45564.2020.9147924 ◽

2020 ◽

Author(s):

Raunak P. Bhattacharyya ◽

Ransalu Senanayake ◽

Kyle Brown ◽

Mykel J. Kochenderfer

Keyword(s):

Parameter Estimation ◽

Driver Behavior ◽

Behavior Prediction ◽

Human Driver

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Human Driver Behavior Prediction based on UrbanFlow*

2020 IEEE International Conference on Robotics and Automation (ICRA) ◽

10.1109/icra40945.2020.9196918 ◽

2020 ◽

Author(s):

Zhiqian Qiao ◽

Jing Zhao ◽

Jin Zhu ◽

Zachariah Tyree ◽

Priyantha Mudalige ◽

...

Keyword(s):

Driver Behavior ◽

Behavior Prediction ◽

Human Driver

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Understanding and Modeling the Human Driver Behavior Based on MPC

IFAC Proceedings Volumes ◽

10.3182/20130904-4-jp-2042.00085 ◽

2013 ◽

Vol 46 (21) ◽

pp. 133-138 ◽

Cited By ~ 3

Author(s):

H.Y. Guo ◽

Y. Ji ◽

T. Qu ◽

H. Chen

Keyword(s):

Driver Behavior ◽

Human Driver ◽

Behavior Based

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Driver-behavior-based robust steering control of unmanned driving robotic vehicle with modeling uncertainties and external disturbance

Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering ◽

10.1177/0954407019895158 ◽

2020 ◽

Vol 234 (6) ◽

pp. 1585-1596

Author(s):

Gang Chen ◽

ShuHua Su

Keyword(s):

Path Planning ◽

Control Method ◽

Driver Behavior ◽

External Disturbance ◽

Steering Control ◽

Planning Strategy ◽

Human Driver ◽

Modeling Uncertainties ◽

Robotic Vehicle ◽

The Stability

In this paper, a steering robust control method based on professional driver behavior with modeling uncertainties and external disturbance is proposed for an unmanned driving robotic vehicle, to realize the accurate and stable steering control like a professional human driver. An unmanned driving robotic vehicle nonlinear dynamics model considering modeling uncertainties and external disturbance is established. A driver behavior model composed of an adaptive preview model, a driver path planning model, and a driver desired yaw rate model is established, with the influences on preview time and driver path planning strategy, respectively. On the basis of this, a robust steering controller based on professional driver behavior is presented, by taking road information, driver reaction delay time, and vehicle driving status as inputs and the servo motor rotation angle of the steering mechanical arm as output. The stability of the control system with modeling uncertainties and external disturbance is proved. A comparison of the analysis results of simulation and experiment among the proposed control method, other existing control methods, and professional human driver demonstrates the effectiveness of the proposed method.

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