• Skip to primary navigation
  • Skip to main content
  • Skip to footer
  • Career
  • Alumni
  • Employers
  • News

Fung Institute for Engineering LeadershipFung Institute for Engineering LeadershipFung Institute for Engineering Leadership

  • Master of Engineering
    • Master of Engineering Program
      • Engineering Departments
      • Program Design
      • Leadership Development
      • Capstone Experience
      • Career Development
      • Learn More
      • How to Apply
  • Fung Fellowship
    • Fung Fellowship

      The Fung Fellowship is shaping a new generation of entrepreneurial leaders focused on transforming health and wellness.

      • Program Overview
    • The Fung Fellowship
    • Executive & Professional Education
  • Partners
    • Partners
    • Become a Partner
    • Propose a Project
    • Recruit a Student
  • Apply
  • About
  • Career
  • Alumni
  • News
Developing an Affordable Alternative for Autonomous Vehicle Localization using High-Definition Radar Images

[Zendar] Developing an Affordable Alternative for Autonomous Vehicle Localization using High-Definition Radar Images

November 15, 2020 by

Team: David Scanlan (IEOR), Pierre-Louis Blossier (ME), Johan Gerfaux (IEOR), Bowen Wang (ME)

Advisors: Jimmy Wang (Zendar), Lou Ggraniou (Zendar), Gabriel Gomes (ME)

Localization of autonomous vehicles on the road currently relies on LiDAR (using lasers) and camera sensors which simultaneously map the environment around the car. Using high-definition radar images, we propose an algorithm to achieve an equivalent performance with a technology that is cheaper, easier to install, and less susceptible to adverse weather conditions such as rain, fog, and snow. This solution, which is based on the hardware and software suite of our partner Zendar, breaks the traditional reliance on costly LiDARs.

Raw data:

Zendar hardware uses high-precision radar technologies that were initially developed for airplanes and UAV remote sensing, and implements them for the first time on autonomous vehicles.

Preprocessing:

In order to make the radar images clearer, our algorithm performs a foreground detection with a specially designed normalization and  lustering of the most informative image pixels.

Mapping:

After fusing GPS positions and consecutive image transformation measurements obtained through correlation algorithms, our method  generates a 2D map that gets rid of the initial radar noise.

Localization:

  • Mapping of the surroundings is performed as described above when the car first discovers an environment.
  • When driving in an already mapped location, our localization algorithm provides a way to get a more precise position of the autonomous vehicle.
  • Even without any help of the GPS, our method can give a good estimation of the position of the car based only on its radar measurements.

Performance summary:

  • Our preprocessing and correlation algorithms can measure the translation and rotation between two consecutive radar images with an average accuracy of 1.3 centimeters and 0.03 degrees.
  • When using our 2D generated map, our localization algorithm makes an average error of 8.2 centimeters in position and 0.24 degrees in orientation with respect to the real trajectory.

Project Brief

Related News:

Video Capstone Project Pitch

Q&A with the capstone winners of 2020 Most Innovative Project

Q&A with the capstone winners of 2020 Most Innovative Project

← View all Capstone Projects

Fung Institute For Engineering Leadership
Shires Hall
2451 Ridge Road Berkeley, CA 94709

Mudd Hall
1798 Scenic Avenue Berkeley, CA 94709

(510) 642-0633
funginstitute@berkeley.edu

Explore

  • Programs
  • Partners
  • Apply
  • Feedback
  • Job Opportunities

Experience

  • About
  • Career
  • Alumni
  • News
  • Donate

Connect

Copyright © 2023 Accessibility • Nondiscrimination • Privacy • Sitemap

berkeley_engineering

uc-berkeley

Copyright © 2023 Accessibility • Nondiscrimination • Privacy • Sitemap

berkeley_engineering

uc-berkeley

Prospective MEng Students

Sign up for our mailing list to receive program news and updates including information sessions, class visits and opportunities to connect with an admissions advisor.