Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Time-Domain Interpretation of PD Control

Time-Domain Interpretation of PD Control

Proportional-Derivative (PD) control is a widely used control method in various engineering systems to enhance stability and performance. In a system with only proportional control, common issues include high maximum overshoot and oscillation, observed in both the error signal and its rate of change. This behavior can be divided into three distinct phases: initial overshoot, subsequent undershoot, and gradual stabilization.
Consider the example of control of motor torque. Initially, a positive...

Controller Configurations

Controller Configurations

Controller configurations are crucial in a car's cruise control system because they manage speed over time to maintain a consistent pace regardless of road conditions, thereby meeting design goals. In traditional control systems, fixed-configuration design involves predetermined controller placement. System performance modifications are known as compensation.
Control-system compensation involves various configurations, most commonly series or cascade compensation, in which the controller...

PD Controller: Design

PD Controller: Design

In automotive engineering, car suspension systems often employ Proportional Derivative (PD) controllers to enhance performance. PD controllers are utilized to adjust the damping force in response to road conditions. A controller, acting as an amplifier with a constant gain, demonstrates proportional control, with output directly mirroring input.
Designing a continuous-data controller requires selecting and linking components like adders and integrators, which are fundamental in Proportional,...

Root-Locus Method

Root-Locus Method

A cruise control system in a car is designed to maintain a specified speed automatically by adjusting the gas pedal. The system continuously measures the vehicle's speed and makes fine adjustments to the pedal to achieve this goal. The root locus method is particularly useful for understanding how the cruise control system's behavior changes under varying conditions, such as when the car goes uphill, downhill, or faces strong wind resistance.
This system can be represented by a block...

Multi-input and Multi-variable systems

Multi-input and Multi-variable systems

Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...

Open and closed-loop control systems

Open and closed-loop control systems

Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Onboard LiDAR-Camera Deployment Optimization for Pavement Marking Distress Fusion Detection.

Sensors (Basel, Switzerland)·2025

Same author

MS-YOLOv8-Based Object Detection Method for Pavement Diseases.

Sensors (Basel, Switzerland)·2024

Same author

Vehicle Detection and Tracking with Roadside LiDAR Using Improved ResNet18 and the Hungarian Algorithm.

Sensors (Basel, Switzerland)·2023

Same author

RDD-YOLOv5: Road Defect Detection Algorithm with Self-Attention Based on Unmanned Aerial Vehicle Inspection.

Sensors (Basel, Switzerland)·2023

Same author

The Economic Value of Water Ecology in Sponge City Construction Based on a Ternary Interactive System.

International journal of environmental research and public health·2022

Same author

A Study on the Impact of Organizing Environmental Awareness and Education on the Performance of Environmental Governance in China.

International journal of environmental research and public health·2022

Search research articles

Related Experiment Video

Updated: Nov 16, 2025

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Published on: August 15, 2020

Modeling driver behavior in the dilemma zone based on stochastic model predictive control.

Wenjun Li¹, Lidong Tan¹, Ciyun Lin^1,2

¹Department of Traffic and Transportation, Jilin University, Changchun, China.

|February 24, 2021

Summary

This study models driver behavior in dilemma zones using stochastic model predictive control (SMPC), enhancing traffic safety. The developed model accurately reflects vehicle dynamics, improving understanding of driver-vehicle-environment systems.

More Related Videos

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Related Experiment Videos

Last Updated: Nov 16, 2025

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

WheelCon: A Wheel Control-Based Gaming Platform for Studying Human Sensorimotor Control

Published on: August 15, 2020

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

Area of Science:

Traffic Safety
Human-Computer Interaction
Mathematical Modeling

Background:

Driver behavior is crucial for traffic safety, particularly in dilemma zones at signalized intersections.
Accurate driver behavior models can enhance traffic signal control and reduce accidents.
Understanding driver-vehicle-environment systems is key to mitigating risks in dilemma zones.

Purpose of the Study:

To develop a mathematical model for driver behavior in dilemma zones.
To incorporate dynamic human cognition and execution characteristics into driver behavior modeling.
To provide a feasible solution for more accurate driver behavior modeling and improved system understanding.

Main Methods:

Utilized stochastic model predictive control (SMPC) for driver behavior modeling.
Developed a framework including perception, decision-making, and operation modules.
Simulated driver behavior in dilemma zones using CarSim software for verification.

Main Results:

The proposed SMPC-based model accurately simulates driver decision-making in dilemma zones.
The model effectively captures the dynamic characteristics of human cognition and execution.
CarSim simulations validated the model's ability to reflect vehicle motion and dynamics.

Conclusions:

The SMPC-based driver behavior model offers a robust approach to understanding and predicting driver actions in dilemma zones.
This research contributes to improved traffic safety by providing a more accurate driver behavior model.
The findings enhance the comprehension of complex interactions within driver-vehicle-environment systems.