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

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

984
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
984
Parallel Processing01:20

Parallel Processing

254
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
254

You might also read

Related Articles

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

Sort by
Same author

Artificial intelligence in geriatric healthcare: a scoping review.

BMC geriatrics·2026
Same author

Sequential immune-related nephritis and pneumonitis during immune checkpoint inhibitor therapy: a case report.

Frontiers in oncology·2026
Same author

Idiopathic mesenteric phlebosclerosis initially misdiagnosed as bowel obstruction: a case report.

Frontiers in medicine·2026
Same author

Novel Monolithic CAVET-HEMT Integration for Inverting-Switch Operation.

ACS omega·2026
Same author

Vertebral involvement in Erdheim-Chester disease: a case report of non-BRAF-driven diagnosis and treatment challenges.

Frontiers in oncology·2026
Same author

Recurrent self-limiting abdominal pain with bowel wall edema misdiagnosed as gastroenteritis: a case report of C1-inhibitor-deficient hereditary angioedema.

Frontiers in medicine·2026

Related Experiment Video

Updated: Sep 20, 2025

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

20.6K

Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges.

Yi Zhou1,2,3,4, Lulu Liu1,3,5,6, Haocheng Zhao1,2,3,4

  • 1Institute of Deep Perception Technology (JITRI), Wuxi 214000, China.

Sensors (Basel, Switzerland)
|June 10, 2022
PubMed
Summary
This summary is machine-generated.

The era of deep learning for automotive radar perception is here, with 4D radar enabling high-resolution imaging. This review provides a holistic overview of deep learning in radar perception, covering challenges and solutions.

Keywords:
automotive radarsautonomous drivingdeep learningmulti-sensor fusionobject detectionradar signal processing

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
Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
07:14

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

7.9K

Related Experiment Videos

Last Updated: Sep 20, 2025

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

20.6K
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
Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar
07:14

Tracking Infiltration Front Depth Using Time-lapse Multi-offset Gathers Collected with Array Antenna Ground Penetrating Radar

Published on: May 1, 2018

7.9K

Area of Science:

  • Automotive Engineering
  • Computer Vision
  • Artificial Intelligence

Background:

  • Automotive radar performance has significantly advanced, with 4D radar offering high-resolution point cloud imaging capabilities.
  • The integration of deep learning into radar perception is becoming increasingly crucial for next-generation autonomous systems.

Purpose of the Study:

  • To provide a comprehensive overview of the deep learning radar perception stack.
  • To synthesize current research across various tasks, including signal processing, data handling, and downstream applications.
  • To highlight and address overlooked challenges in deep radar perception.

Main Methods:

  • Review of existing literature on deep learning for automotive radar perception.
  • Analysis of network structures adapted to radar domain knowledge for tasks like depth/velocity estimation, object detection, and sensor fusion.
  • Identification and summarization of challenges such as multi-path effects, uncertainty, and adverse weather.

Main Results:

  • A holistic view of the deep radar perception pipeline, from signal processing to sensor fusion.
  • Explanation of how network architectures are tailored for radar data.
  • Summary of key challenges and existing approaches to mitigate them.

Conclusions:

  • Deep learning is pivotal for advancing automotive radar perception, especially with 4D radar technology.
  • Addressing challenges like multi-path, uncertainty, and adverse weather is essential for robust radar perception systems.
  • This review serves as a foundational resource for future research in deep radar perception.