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 Experiment Video

Updated: Oct 12, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.7K

A Review of Deep Learning-Based Methods for Pedestrian Trajectory Prediction.

Bogdan Ilie Sighencea1, Rareș Ion Stanciu1, Cătălin Daniel Căleanu1

  • 1Applied Electronics Department, Faculty of Electronics, Telecommunications, and Information Technologies, Politehnica University Timișoara, 300223 Timișoara, Romania.

Sensors (Basel, Switzerland)
|November 27, 2021
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Images Versus Videos in Contrast-Enhanced Ultrasound for Computer-Aided Diagnosis.

Sensors (Basel, Switzerland)·2025
Same author

Deep Neural Architectures for Contrast Enhanced Ultrasound (CEUS) Focal Liver Lesions Automated Diagnosis.

Sensors (Basel, Switzerland)·2021
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

This review covers deep learning for pedestrian trajectory prediction, a key challenge for advanced driver assistance systems. It analyzes methods, datasets, and applications, identifying future research directions.

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Robotics

Background:

  • Pedestrian trajectory prediction is crucial for automotive safety and advanced driver assistance systems (ADAS).
  • Accurate prediction of pedestrian movements remains a significant technological challenge in various applications.
  • Advancements in sensor technology and signal processing are enhancing prediction capabilities.

Purpose of the Study:

  • To review recent deep learning-based solutions for pedestrian trajectory prediction.
  • To provide an overview of sensors, processing methodologies, datasets, and performance metrics.
  • To identify research gaps and suggest future research directions in the field.

Main Methods:

  • Review of state-of-the-art deep learning models for trajectory prediction.
Keywords:
autonomous vehiclesdeep learningpedestrian behaviorsensor technologiestrajectory prediction

More Related Videos

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.5K

Related Experiment Videos

Last Updated: Oct 12, 2025

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

13.7K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.5K
  • Analysis of sensor technologies and signal processing techniques used.
  • Compilation and overview of relevant datasets and evaluation metrics.

    • Main Results:

    • Deep learning methods show significant promise in improving pedestrian trajectory prediction.
    • A comprehensive overview of current methodologies, datasets, and applications is presented.
    • Key research gaps and potential future research avenues are highlighted.

    Conclusions:

    • Deep learning is a pivotal technology for advancing pedestrian trajectory prediction.
    • Further research is needed to address existing challenges and explore new directions.
    • This review serves as a valuable resource for researchers and developers in the field.