Jove
Visualize
Contact Us

Related Concept Videos

Autonomic Nervous System01:22

Autonomic Nervous System

12.4K
The autonomic nervous system (ANS) is a critical component of the peripheral nervous system, primarily responsible for regulating involuntary bodily functions and maintaining homeostasis. It functions in tandem with the central nervous system (CNS) to seamlessly coordinate various physiological processes without the need for conscious control.
The ANS comprises two main divisions: the sympathetic and parasympathetic divisions. These divisions function antagonistically to maintain a dynamic...
12.4K
Energy to Drive Translocation01:37

Energy to Drive Translocation

2.7K
Mitochondrial protein import is powered by two distinct energy sources: ATP hydrolysis and electrochemical potential across the inner membrane. Newly synthesized precursors are bound by cytosolic chaperones of the Hsp70 family, which guide them to the import receptors on the mitochondrial surface. Utilizing the energy of ATP hydrolysis, Hsp70 chaperones transfer these precursors to the TOM receptors on the mitochondrial outer membrane.
Generally, polypeptides are unfolded by two distinct...
2.7K
Machines01:19

Machines

563
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
563
Autonomic Nervous System: Overview01:26

Autonomic Nervous System: Overview

7.4K
The human nervous system is divided into two main parts: the central nervous system (CNS) and the peripheral nervous system (PNS). The CNS is composed of the brain and spinal cord, while the PNS contains nerve cells, clusters of nerve cells, and the sensory receptors that are outside the CNS. The PNS has two types of nerve cells: sensory (afferent) and motor (efferent). Sensory cells send signals to the CNS from receptors, and motor cells carry signals from the CNS to organs, muscles, and...
7.4K
Disorders of the Autonomic Nervous System01:18

Disorders of the Autonomic Nervous System

1.4K
The autonomic nervous system (ANS) is an intricate network of nerves that controls functions such as the regulation of heart rate, digestion, and blood pressure regulation. When this system malfunctions, it can lead to various disorders that affect multiple bodily functions. One common feature of many autonomic disorders is the involvement of smooth blood vessels, which play a crucial role in regulating blood flow throughout the body.
Raynaud's disease, also known as Raynaud's...
1.4K
Machines: Problem Solving II01:30

Machines: Problem Solving II

652
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
652

You might also read

Related Articles

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

Sort by
Same author

Leveraging Large Language Models for Accurate Retrieval of Patient Information From Medical Reports: Systematic Evaluation Study.

JMIR AI·2025
Same author

Development of a urinometer for automatic measurement of urine flow in catheterized patients.

PloS one·2023
Same author

Web and MATLAB-Based Platform for UAV Flight Management and Multispectral Image Processing.

Sensors (Basel, Switzerland)·2022
Same author

Advanced Driver Assistance Systems (ADAS) Based on Machine Learning Techniques for the Detection and Transcription of Variable Message Signs on Roads.

Sensors (Basel, Switzerland)·2021
Same author

Dataset Construction from Naturalistic Driving in Roundabouts.

Sensors (Basel, Switzerland)·2020
Same author

Think Aloud Protocol Applied in Naturalistic Driving for Driving Rules Generation.

Sensors (Basel, Switzerland)·2020
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 Experiment Video

Updated: Jan 24, 2026

Micro-drive Array for Chronic in vivo Recording: Drive Fabrication
14:03

Micro-drive Array for Chronic in vivo Recording: Drive Fabrication

Published on: April 20, 2009

25.8K

Machine Learning Techniques for Undertaking Roundabouts in Autonomous Driving.

Laura García Cuenca1, Javier Sanchez-Soriano2, Enrique Puertas3

  • 1Science, Computing and Technology, Universidad Europa de Madrid, Calle Tajo s/n, Villaviciosa de Odón, 28670 Madrid, Spain. laura.garcia@universidadeuropea.es.

Sensors (Basel, Switzerland)
|May 30, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a machine learning method for autonomous vehicles to navigate roundabouts. It develops predictive models for speed and steering, creating actionable rules for safe and efficient autonomous driving maneuvers.

Keywords:
autonomous drivingdata miningdeep learninglinear regressionmachine learningroundaboutssensor fusionsupport vector machines

More Related Videos

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.4K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.4K

Related Experiment Videos

Last Updated: Jan 24, 2026

Micro-drive Array for Chronic in vivo Recording: Drive Fabrication
14:03

Micro-drive Array for Chronic in vivo Recording: Drive Fabrication

Published on: April 20, 2009

25.8K
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

7.4K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.4K

Area of Science:

  • Robotics and Artificial Intelligence
  • Transportation Engineering
  • Machine Learning

Background:

  • Autonomous vehicles require sophisticated control systems for complex maneuvers like roundabouts.
  • Existing methods may lack the adaptability and precision needed for real-world roundabout navigation.

Purpose of the Study:

  • To develop a machine learning-based technique for autonomous vehicle roundabout navigation.
  • To generate actionable rules for vehicle speed and steering control during roundabout maneuvers.

Main Methods:

  • Utilized supervised learning algorithms including support vector machines, linear regression, and deep learning.
  • Built predictive models for vehicle speeds and steering angles using driver-vehicle interaction data.
  • Incorporated traffic environment data, including roundabout geometry and lane count from Open-Street-Maps and video processing.

Main Results:

  • Successfully generated rules of action for autonomous vehicle speed and steering.
  • Developed a predictive model capable of simulating roundabout maneuvers.
  • Demonstrated the feasibility of using machine learning for autonomous roundabout navigation.

Conclusions:

  • The proposed machine learning technique enables autonomous vehicles to perform complex roundabout maneuvers.
  • The generated rules provide a foundation for developing robust autonomous driving systems.
  • This approach enhances the safety and efficiency of autonomous vehicle navigation in intricate traffic scenarios.