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

Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the rated...
Pharmacodynamic Models: Emax Drug–Concentration Effect Model01:18

Pharmacodynamic Models: Emax Drug–Concentration Effect Model

The Emax drug-concentration effect model is central to pharmacodynamics in drug discovery and development. This model is predicated on the receptor occupancy theory, which posits that the effect of a drug is directly related to the number of receptors occupied by the drug and the resultant complex formation.The model describes the reversible interaction between a drug (C) and a receptor (R) to form a drug-receptor complex (RC). The kinetics of this interaction are quantified by an equation that...
Introduction to Vertical Curves01:24

Introduction to Vertical Curves

Vertical curves are parabolic transitions that connect different grades on highways and railroads, ensuring a smooth alignment between back and forward tangents. The back tangent represents the initial grade, while the forward tangent defines the subsequent grade. These curves can be symmetrical, with equal tangent lengths, or nonsymmetrical, with varying lengths. The key points defining a vertical curve include the Point of Vertical Intersection (P.V.I.), where the tangents meet; the Point of...
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
Social Traps01:41

Social Traps

Social traps are negative situations where people get caught in a direction or relationship that later proves to be unpleasant, with no easy way to back out of or avoid. The concept was orignally introduced by John Platt who applied psychology to Garrett Hardin's "Tragedy of the Commons", where in New England herd owners could let their cattle graze in the common ground. This situation seems like a good idea, but an individual could have an advantage. If they owned more cows, the larger...
Two-Compartment Open Model: Overview01:05

Two-Compartment Open Model: Overview

Multicompartmental models are crucial tools in pharmacokinetics, providing a framework to understand how drugs move within the body. The two-compartment model is a crucial subtype, segmenting the body into central and peripheral compartments. The central compartment represents areas with high blood flow, such as plasma and highly perfused organs like the kidneys and liver, while the peripheral compartment signifies tissues with lower blood flow, like adipose tissue and muscle tissue.
The...

You might also read

Related Articles

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

Sort by
Same author

YOLO-DCRCF: An Algorithm for Detecting the Wearing of Safety Helmets and Gloves in Power Grid Operation Environments.

Journal of imaging·2025
Same author

Evolution of the Properties and Composition of Heavy Oil by Injecting Dry Boiler Flue Gas.

ACS omega·2021
Same author

Baicalin regulates mRNA expression of VEGF-c, Ang-1/Tie2, TGF-β and Smad2/3 to inhibit wound healing in streptozotocin-induced diabetic foot ulcer rats.

Journal of biochemical and molecular toxicology·2021
Same author

Performance Analysis of Wireless Information Surveillance in Machine-Type Communication at Finite Blocklength Regime.

Sensors (Basel, Switzerland)·2019
Same author

Association among activities of daily living, instrumental activities of daily living and health-related quality of life in elderly Yi ethnic minority.

BMC geriatrics·2017
Same author

Broadband wavelength converters with flattop responses based on cascaded second-harmonic generation and difference frequency generation in Bessel-chirped gratings.

Optics express·2016

Related Experiment Video

Updated: May 14, 2026

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

EA-UNET: An Enhanced and Efficient Model for Left-Turn Lane.

Haowei Wang1, Haixin Liu1, Fei Wang1

  • 1School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China.

Sensors (Basel, Switzerland)
|May 13, 2026
PubMed
Summary

A new deep learning model, EA-UNet, accurately detects left-turn lanes for autonomous vehicles. This lightweight network improves efficiency and robustness in complex urban environments.

Keywords:
Convolutional Block Attention ModuleEA-UNetautonomous vehicleleft-turn laneslightweight network

More Related Videos

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
14:55

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

Related Experiment Videos

Last Updated: May 14, 2026

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
11:41

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation

Published on: February 1, 2020

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
14:55

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

Published on: January 20, 2023

Area of Science:

  • Computer Vision
  • Deep Learning
  • Autonomous Systems

Background:

  • Left-turn lanes are crucial for urban intersection safety.
  • Current lane detection methods struggle with accuracy, computational cost, and environmental factors.
  • Autonomous vehicle navigation demands precise and efficient lane detection.

Purpose of the Study:

  • To develop a lightweight deep convolutional neural network for accurate left-turn lane detection.
  • To overcome the limitations of existing semantic segmentation algorithms.
  • To enhance the safety and efficiency of autonomous vehicle navigation.

Main Methods:

  • Proposed EA-UNet, a lightweight deep convolutional neural network.
  • Replaced the standard U-Net encoder with EfficientNet-B0 for improved feature extraction.
  • Introduced a novel MP-ASPP module with CBAM for refined attention mechanisms.
  • Created a comprehensive real-world dataset for left-turn lane segmentation.

Main Results:

  • EA-UNet demonstrated superior performance compared to baseline U-Net and other state-of-the-art models.
  • Achieved accurate and efficient segmentation of left-turn lanes.
  • Showcased robustness in complex urban intersection scenes.

Conclusions:

  • EA-UNet offers a significant advancement in left-turn lane detection for autonomous vehicles.
  • The proposed model provides a lightweight, accurate, and efficient solution.
  • EA-UNet enhances the reliability of autonomous navigation systems in challenging conditions.