Jove
Visualize
Contact Us

Related Concept Videos

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

203
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...
203
Rolling Resistance: Problem Solving01:17

Rolling Resistance: Problem Solving

516
Rolling resistance, also known as rolling friction, is the force that resists the motion of a rolling object, such as a wheel, tire, or ball, when it moves over a surface. It is caused by the deformation of the object and the surface in contact with each other, as well as other factors like internal friction, hysteresis, and energy losses within the materials. Rolling resistance opposes the object's motion, requiring additional energy to overcome it and maintain movement. In practical...
516
Hierarchy of Motor Control01:18

Hierarchy of Motor Control

4.1K
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.
4.1K
Perception01:28

Perception

671
Perception is a fundamental psychological process that enables individuals to organize, interpret, and consciously experience sensory information. This process is crucial for understanding and interacting with the world around us. It includes both bottom-up and top-down processing, each playing a distinct role in how we perceive our environment.
Bottom-up processing begins at the sensory level, where receptors detect external environmental stimuli. These could include the tactile sensation of...
671
Motor Units01:13

Motor Units

5.9K
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...
5.9K
One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

574
In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
A one-degree-of-freedom system is defined by an independent variable that determines its state and behavior. One example of a one-degree-of-freedom system is a simple harmonic oscillator, such as a...
574

You might also read

Related Articles

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

Sort by
Same author

Towards Interpretable Camera and LiDAR Data Fusion for Autonomous Ground Vehicles Localisation.

Sensors (Basel, Switzerland)·2022
Same author

Residual Q-Networks for Value Function Factorizing in Multiagent Reinforcement Learning.

IEEE transactions on neural networks and learning systems·2022
Same author

Evaluating the Learning Procedure of CNNs through a Sequence of Prognostic Tests Utilising Information Theoretical Measures.

Entropy (Basel, Switzerland)·2022
Same author

Understanding Dilated Mathematical Relationship between Image Features and the Convolutional Neural Network's Learnt Parameters.

Entropy (Basel, Switzerland)·2022
Same author

Overcoming Challenges of Applying Reinforcement Learning for Intelligent Vehicle Control.

Sensors (Basel, Switzerland)·2021
Same author

A Multimodal Data Processing System for LiDAR-Based Human Activity Recognition.

IEEE transactions on cybernetics·2021
Same journal

An Evolutionary Algorithm Assisted by an Ensemble of Pareto-Optimal Surrogate Models.

IEEE transactions on cybernetics·2026
Same journal

A Quantum Self-Attention Neural Network Model on Quantum Circuits.

IEEE transactions on cybernetics·2026
Same journal

Semi-Explicit Solution of Some Discrete-Time Higher-Order-Cost Mean-Field-Type Control.

IEEE transactions on cybernetics·2026
Same journal

A Novel One-Step Small Object Detector for Autonomous Aerial Vehicles.

IEEE transactions on cybernetics·2026
Same journal

Online Data-Driven-Based Optimal Output Tracking Control Without Initial Stabilizing Policy.

IEEE transactions on cybernetics·2026
Same journal

Digital Redesign-Based Interval State Estimation for Continuous Systems With Aperiodic Discrete Measurements.

IEEE transactions on cybernetics·2026
See all related articles
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: Oct 17, 2025

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

9.3K

A Multimodal Perception-Driven Self Evolving Autonomous Ground Vehicle.

Jamie Roche, Varuna De-Silva, Ahmet Kondoz

    IEEE Transactions on Cybernetics
    |October 8, 2021
    PubMed
    Summary
    This summary is machine-generated.

    A novel self-evolving autonomous ground vehicle (AGV) framework enhances free space detection (FSD) using active machine learning and sensor fusion. This approach reduces reliance on large datasets and improves safety in diverse environments.

    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

    4.6K
    MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions
    09:46

    MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions

    Published on: May 10, 2012

    12.8K

    Related Experiment Videos

    Last Updated: Oct 17, 2025

    Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
    07:05

    Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

    Published on: October 27, 2016

    9.3K
    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

    4.6K
    MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions
    09:46

    MPI CyberMotion Simulator: Implementation of a Novel Motion Simulator to Investigate Multisensory Path Integration in Three Dimensions

    Published on: May 10, 2012

    12.8K

    Area of Science:

    • Robotics and Artificial Intelligence
    • Computer Vision
    • Machine Learning

    Background:

    • Automated driving functions, particularly free space detection (FSD), often rely on convolutional neural networks (CNNs).
    • Limited or non-diverse training datasets for CNNs can compromise the safety of autonomous ground vehicles (AGVs), especially in unstructured environments.
    • Existing methods struggle with robustness and adaptability in varied, real-world conditions.

    Purpose of the Study:

    • To develop an autonomous ground vehicle (AGV) capable of seamless indoor/outdoor navigation for realistic data collection.
    • To introduce a self-evolving free space detection (FSD) framework utilizing online active machine learning (ML) and sensor fusion.
    • To enhance the robustness and reduce data dependency for FSD in autonomous driving systems.

    Main Methods:

    • An AGV was engineered for multimodal data stream collection across diverse indoor and outdoor settings.
    • A self-evolving FSD framework was implemented, integrating online active ML paradigms with sensor data fusion.
    • Image data was cross-validated against a reliable ultrasound data stream, followed by sensor fusion for enhanced robustness.

    Main Results:

    • The proposed framework demonstrated superior performance compared to the state-of-the-art DeepLabV3+ semantic segmentation model.
    • The self-evolving capability enabled the system to learn free space dynamically, reducing the need for extensive pre-existing datasets.
    • Sensor fusion, particularly with ultrasound data, significantly improved the reliability and accuracy of free space classification.

    Conclusions:

    • The developed AGV and self-evolving FSD framework offer a robust solution for autonomous navigation challenges.
    • Active ML and sensor fusion significantly enhance FSD accuracy and reduce reliance on large, curated datasets.
    • This approach paves the way for safer and more adaptable autonomous driving systems in complex, real-world scenarios.