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

PI Controller: Design

PI Controller: Design

Proportional Integral (PI) controllers are a fundamental component in modern control systems, widely used to enhance performance and mitigate steady-state errors. They are particularly effective in applications such as automatic brightness adjustment on smartphones, where they excel at mitigating steady-state errors for step-function inputs. Unlike PD controllers, which require time-varying errors to function optimally, PI controllers leverage their integral component to address residual...

You might also read

Related Articles

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

Sort by

Same author

Disentangling autoencoders and spherical harmonics for efficient shape classification in crystal growth simulations.

Communications physics·2025

Same author

Discovering fully semantic representations via centroid- and orientation-aware feature learning.

Nature machine intelligence·2025

Same author

EMinsight: a tool to capture cryoEM microscope configuration and experimental outcomes for analysis and deposition.

Acta crystallographica. Section D, Structural biology·2024

Same author

Triple-kernel gated attention-based multiple instance learning with contrastive learning for medical image analysis.

Applied intelligence (Dordrecht, Netherlands)·2023

Same author

Parameter inversion of a polydisperse system in small-angle scattering.

Journal of applied crystallography·2022

Same author

Keyhole fluctuation and pore formation mechanisms during laser powder bed fusion additive manufacturing.

Nature communications·2022

Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026

Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026

Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026

Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026

Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026

Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Nov 21, 2025

Image-based Lagrangian Particle Tracking in Bed-load Experiments

Image-based Lagrangian Particle Tracking in Bed-load Experiments

Published on: July 20, 2017

A Multilane Tracking Algorithm Using IPDA with Intensity Feature.

Behzad Akbari¹, Jeyan Thiyagalingam², Richard Lee³

¹ECE Department, McMaster University, Hamilton, ON L8S 4L8, Canada.

Sensors (Basel, Switzerland)

|January 14, 2021

Summary

This summary is machine-generated.

This study introduces a novel multitarget tracking approach for robust multiple lane detection in autonomous vehicles. The method improves accuracy and frame rates across diverse lighting conditions and datasets.

Keywords:

Hough transform curve fitting integrated probability data association (IPDA)maximum a posteriori (MAP)multilane tracking probability density function (PDF)

More Related Videos

Stereo-Imaging System DLT Calibration to Capture 3D In Situ Displacements of Stretched Peripheral Nerves

Stereo-Imaging System DLT Calibration to Capture 3D In Situ Displacements of Stretched Peripheral Nerves

Published on: January 12, 2024

Visualizing Protein-DNA Interactions in Live Bacterial Cells Using Photoactivated Single-molecule Tracking

Visualizing Protein-DNA Interactions in Live Bacterial Cells Using Photoactivated Single-molecule Tracking

Published on: March 10, 2014

Related Experiment Videos

Last Updated: Nov 21, 2025

Image-based Lagrangian Particle Tracking in Bed-load Experiments

Image-based Lagrangian Particle Tracking in Bed-load Experiments

Published on: July 20, 2017

Stereo-Imaging System DLT Calibration to Capture 3D In Situ Displacements of Stretched Peripheral Nerves

Stereo-Imaging System DLT Calibration to Capture 3D In Situ Displacements of Stretched Peripheral Nerves

Published on: January 12, 2024

Visualizing Protein-DNA Interactions in Live Bacterial Cells Using Photoactivated Single-molecule Tracking

Visualizing Protein-DNA Interactions in Live Bacterial Cells Using Photoactivated Single-molecule Tracking

Published on: March 10, 2014

Area of Science:

Computer Vision
Robotics
Artificial Intelligence

Background:

Autonomous vehicle navigation relies heavily on accurate lane detection.
Existing lane detection methods struggle with consistency under varying lighting and across multiple frames.
Detecting multiple lane markings simultaneously presents a significant challenge.

Purpose of the Study:

To propose a novel approach for detecting multiple lane markings consistently across numerous frames and diverse lighting conditions.
To treat multiple lane detection as a multitarget tracking problem in space and time.
To enhance lane detection performance for autonomous driving systems.

Main Methods:

Utilizes the integrated probabilistic data association filter (IPDAF) as the core tracking algorithm.
Employs Hough transform with pixel intensity as an augmented feature for grouping lane markings.
Represents detected lane markings as splines and tracks their control points over time.
Evaluates the approach on Caltech and TuSimple lane detection datasets, comparing against model-based and machine-learning methods.

Main Results:

Achieved significant improvements over model-based approaches in true positive, false positive, and false positives per frame rates.
Demonstrated superior performance compared to state-of-the-art machine learning techniques, with substantial gains in accuracy and frame rates.
Showcased notable reductions in false positives and false negatives.
Maintained explainability of the detection process.

Conclusions:

The proposed multitarget tracking approach offers a robust and explainable solution for multiple lane detection in autonomous vehicles.
The method effectively handles challenges posed by varying lighting conditions and the need for cross-frame consistency.
This approach represents a significant advancement in lane detection technology for enhanced autonomous driving safety and reliability.