Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Open and closed-loop control systems01:17

Open and closed-loop control systems

1.6K
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...
1.6K
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

1.1K
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
1.1K
Controller Configurations01:22

Controller Configurations

354
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...
354
Hierarchy of Motor Control01:18

Hierarchy of Motor Control

5.9K
The hierarchy of motor control refers to the different levels of organization and processing involved in controlling movement in the body. These levels range from higher cortical areas involved in planning and decision-making to lower spinal cord reflexes that respond automatically to external stimuli.
5.9K
Motor Units01:13

Motor Units

7.5K
The motor unit is a fundamental component of the neuromuscular system and plays a crucial role in coordinating muscle contractions. It consists of a somatic motor neuron, which connects and controls multiple skeletal muscle fibers, forming a single functional segment. The axon of the motor neuron branches out and establishes synaptic connections known as neuromuscular junctions with individual muscle fibers within the motor unit.
Motor units come in different sizes, with smaller units...
7.5K
Motor Units00:46

Motor Units

61.7K
A motor unit consists of two main components: a single efferent motor neuron (i.e., a neuron that carries impulses away from the central nervous system) and all of the muscle fibers it innervates. The motor neuron may innervate multiple muscle fibers, which are single cells, but only one motor neuron innervates a single muscle fiber.
61.7K

You might also read

Related Articles

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

Sort by
Same author

Predictive Neural Network Modeling for Almond Harvest Dust Control.

Sensors (Basel, Switzerland)·2024
Same author

Comparison of two schedules of hypo-fractionated radiotherapy in locally advanced head-and-neck cancers.

Journal of cancer research and therapeutics·2022
Same author

Numerical study of hemodynamics in a complete coronary bypass with venous and arterial grafts and different degrees of stenosis.

Computer methods in biomechanics and biomedical engineering·2020
Same author

Nitrogen dioxide and asthma emergency department visits in California, USA during cold season (November to February) of 2005 to 2015: A time-stratified case-crossover analysis.

The Science of the total environment·2020
Same author

Pros and Cons of Adding of Neoadjuvant Chemotherapy to Standard Concurrent Chemoradiotherapy in Cervical Cancer: A Regional Cancer Center Experience.

Journal of obstetrics and gynaecology of India·2016
Same author

A CAD-CAM prosthodontic option and gingival zenith position for a rotated maxillary right central incisor: An evaluation.

Indian journal of dental research : official publication of Indian Society for Dental Research·2012

Related Experiment Video

Updated: Jan 18, 2026

Operation of the Collaborative Composite Manufacturing CCM System
10:09

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

7.1K

Online Centralized MPC for Lane Merging in Vehicle Platoons.

Shila Alizadehghobadi1, Mukesh Singhal2, Reza Ehsani1

  • 1Department of Mechanical Engineering, University of California, Merced, CA 95343, USA.

Sensors (Basel, Switzerland)
|September 13, 2025
PubMed
Summary

A new centralized Model Predictive Control (MPC) framework enables online trajectory tracking for autonomous vehicle platoons. This approach optimizes lane merging, reducing energy consumption by up to 40% and enhancing adaptability to dynamic conditions.

Keywords:
centralized MPClane mergingonlineplatoonprediction horizonreordering

More Related Videos

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

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

Published on: December 18, 2020

4.9K
A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

6.3K

Related Experiment Videos

Last Updated: Jan 18, 2026

Operation of the Collaborative Composite Manufacturing CCM System
10:09

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

7.1K
Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
07:15

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

Published on: December 18, 2020

4.9K
A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

6.3K

Area of Science:

  • Robotics and Control Systems
  • Autonomous Vehicle Technology
  • Traffic Engineering

Background:

  • Efficient and safe lane merging in autonomous vehicles is crucial for reducing traffic congestion and improving road safety.
  • Existing Model Predictive Control (MPC) approaches often rely on offline planning, limiting their adaptability to dynamic scenarios.
  • The need for flexible, online control strategies for multi-vehicle platooning during merging maneuvers is evident.

Purpose of the Study:

  • To present a centralized MPC framework for online trajectory tracking in multi-vehicle platoons during lane merging.
  • To investigate the impact of prediction horizon and platoon size on the feasibility and efficiency of merging maneuvers.
  • To demonstrate the framework's ability to handle dynamic constraints and disturbances for collision-free operation.

Main Methods:

  • Development of a centralized Model Predictive Control (MPC) framework for online trajectory optimization.
  • Analysis of prediction horizon and platoon size effects on merging maneuver feasibility and efficiency.
  • Evaluation of the algorithm's performance under dynamic constraints and disturbances.

Main Results:

  • An optimal prediction horizon was identified, minimizing braking and acceleration, leading to 35-40% energy savings.
  • Increasing prediction horizon beyond feasibility requirements can alter vehicle sequencing within the platoon, a capability lacking in offline methods.
  • A significant increase in minimum feasible prediction horizon was observed with larger platoon sizes.

Conclusions:

  • The proposed online MPC framework offers enhanced flexibility and adaptability for autonomous vehicle lane merging compared to offline approaches.
  • The study highlights the critical role of prediction horizon in optimizing energy efficiency and enabling dynamic vehicle re-sequencing.
  • The findings underscore the scalability challenges and increased control complexity associated with larger autonomous vehicle platoons.