ADAS & Autonomous Vehicle International
  • News
    • A-L
      • ADAS
      • AI & Sensor Fusion
      • Business
      • Connectivity
      • Cybersecurity
      • Expo
      • HMI
      • Last-mile delivery
      • Legislation & Standards
      • Localization/GNSS
    • M-Z
      • Mapping
      • Off-Highway
      • Robo-Taxis
      • Sensors
      • Shared Mobility
      • Safety
      • Simulation
      • Testing
      • Trucks
      • V2X
  • Features
  • Online Magazines
    • January 2025
    • September 2024
    • April 2024
    • January 2024
    • Subscribe
  • Opinion
  • Videos
  • Supplier Spotlight
  • Events
LinkedIn Facebook Twitter
  • Automotive Interiors
  • Automotive Testing
  • Automotive Powertrain
  • Professional Motorsport
  • Tire Technology
  • Media Pack
    • 2026 Media Pack
    • 2025 Media Pack
LinkedIn Facebook
Subscribe
ADAS & Autonomous Vehicle International
  • News
      • ADAS
      • AI & Sensor Fusion
      • Business
      • Connectivity
      • Cybersecurity
      • Expo
      • HMI
      • Last-mile delivery
      • Legislation & Standards
      • Localization/GNSS
      • Mapping
      • Off-Highway
      • Robo-Taxis
      • Sensors
      • Shared Mobility
      • Safety
      • Simulation
      • Testing
      • Trucks
      • V2X
  • Features
  • Online Magazines
    1. April 2025
    2. January 2025
    3. September 2024
    4. April 2024
    5. January 2024
    6. Subscribe
    Featured
    April 15, 2025

    In this Issue – April 2025

    Online Magazines By Web Team
    Recent

    In this Issue – April 2025

    April 15, 2025

    In this Issue – January 2025

    November 29, 2024

    In this Issue – September 2024

    July 23, 2024
  • Opinion
  • Videos
  • Supplier Spotlight
  • Events
  • Awards
    • About
    • Shortlist
    • Judges
LinkedIn Facebook
Subscribe
ADAS & Autonomous Vehicle International
Sensors

Japanese researchers improve radar recognition of humans in NLOS situations

Lawrence ButcherBy Lawrence ButcherOctober 4, 20213 Mins Read
Share
LinkedIn Twitter Facebook Email

The University of Electro-Communications (UEC) in Tokyo, Japan, has released details of its latest research into radar recognition of humans for autonomous driving applications.

The research found that radar-based sensors have emerged as an essential component of driver assistance systems and self-driving vehicles, as they can robustly distinguish nearby pedestrians and other traffic-relevant objects. However, in addition to being applicable in bad weather, artificial recognition systems need to be capable of dealing with so-called non-line-of-sight (NLOS) situations, when the line of sight between detector and object is obstructed. In traffic, NLOS situations occur when pedestrians are blocked from sight; for example, a child behind a parked car, about to run suddenly into the street.

Associate professor Shouhei Kidera and his research team at UEC claim to have developed a radar-based detection method for recognizing humans in NLOS situations. The scheme is based on reflection and diffraction signal analysis and machine learning techniques.

The researchers performed radar experiments in an anechoic chamber, with a metallic plate placed in the chamber so that an NLOS situation arose when a target object moved behind the plate from the radar’s point of view. A 24GHz radar and two target objects were used in the experiments: a 30cm-long metallic cylinder and a human wearing light-colored clothes. Three regimes were investigated: complete NLOS, partially NLOS (target object positioned at the border between the NLOS and the LOS zone) and complete LOS. The signals received by the detector were intrinsically different for the metallic cylinder and the human. Even if a human stands still, breathing and small movements related to posture control cause changes in the reflected wave signals. The scientists found that the differences are enhanced by diffraction effects: the ‘bending’ of waves around the edges of the metallic plate.

The researchers applied a machine-learning algorithm to the reflection and diffraction signals to let their sensing device learn the difference between a human and a non-human object. A recognition rate of up to 80% was achieved. They also performed experiments with an actual car as the shielding object, which led to similar results and additional understanding of the dependence of the recognition success rate on the radar’s position relative to the target. Also, by carrying out additional experiments with a human performing a stepping motion, the scientists were able to recognize whether a human is standing still or walking, even in complete NLOS situations.

The results, remarked Kidera, signify an important step forward toward feasible self-driving car technology, though he noted that “there should be further investigation using other classifiers or features, which is our important future work”.

The full paper can be found here.

Share. Twitter LinkedIn Facebook Email
Previous ArticleUS 5G smart city to harness new cloud resources
Next Article Sibros to provide Volta Trucks with connected vehicle systems

Related Posts

Sensors

NXP unveils third-generation imaging radar processors

May 16, 20252 Mins Read
Safety

Elektrobit and Metoak partner on SDV safety ecosystem development

May 12, 20252 Mins Read
Safety

Zoox issues software recall following Las Vegas incident

May 8, 20253 Mins Read
Latest News

AAVI AWARDS: Winners announced!

May 21, 2025

EXPO NEWS: Gaia-X 4 PLC-AAD mini-conference reveals project results

May 21, 2025

EXPO NEWS: Highlights from Day 2 at ADAS & Autonomous Vehicle Technology Expo Europe

May 21, 2025
FREE WEEKLY E-NEWSLETTER

Receive breaking stories and features in your inbox each week, for free


Enter your email address:


Our Social Channels
  • Facebook
  • LinkedIn
Getting in Touch
  • Free Weekly E-Newsletters
  • Meet the Editors
  • Contact Us
  • Media Pack
    • 2026 Media Pack
    • 2025 Media Pack
RELATED UKI TOPICS
  • Automotive Interiors
  • Automotive Testing
  • Automotive Powertrain
  • Professional Motorsport
  • Tire Technology
  • Media Pack
    • 2026 Media Pack
    • 2025 Media Pack
© 2025 UKi Media & Events a division of UKIP Media & Events Ltd
  • Terms and Conditions
  • Privacy Policy
  • Cookie Policy
  • Notice & Takedown Policy
  • Site FAQs

Type above and press Enter to search. Press Esc to cancel.

We use cookies on our website to give you the most relevant experience by remembering your preferences and repeat visits. By clicking “Accept”, you consent to the use of ALL the cookies.
Cookie settingsACCEPT
Manage consent

Privacy Overview

This website uses cookies to improve your experience while you navigate through the website. Out of these, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. We also use third-party cookies that help us analyze and understand how you use this website. These cookies will be stored in your browser only with your consent. You also have the option to opt-out of these cookies. But opting out of some of these cookies may affect your browsing experience.
Necessary
Always Enabled

Necessary cookies are absolutely essential for the website to function properly. These cookies ensure basic functionalities and security features of the website, anonymously.

CookieDurationDescription
cookielawinfo-checbox-analytics11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Analytics".
cookielawinfo-checbox-functional11 monthsThe cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional".
cookielawinfo-checbox-others11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other.
cookielawinfo-checkbox-necessary11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookies is used to store the user consent for the cookies in the category "Necessary".
cookielawinfo-checkbox-performance11 monthsThis cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Performance".
viewed_cookie_policy11 monthsThe cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. It does not store any personal data.

Functional

Functional cookies help to perform certain functionalities like sharing the content of the website on social media platforms, collect feedbacks, and other third-party features.

Performance

Performance cookies are used to understand and analyze the key performance indexes of the website which helps in delivering a better user experience for the visitors.

Analytics

Analytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics the number of visitors, bounce rate, traffic source, etc.

Advertisement

Advertisement cookies are used to provide visitors with relevant ads and marketing campaigns. These cookies track visitors across websites and collect information to provide customized ads.

Others

Other uncategorized cookies are those that are being analyzed and have not been classified into a category as yet.

SAVE & ACCEPT