R-fiducial: Millimeter Wave Radar Fiducials for Sensing Traffic Infrastructure
Planning to Explore via Self-Supervised World Models
R-fiducial: Millimeter Wave Radar Fiducials for Sensing Traffic Infrastructure

Kshitiz Bansal
ksbansal@ucsd.edu
Manideep Dunna
mdunna@ucsd.edu
Sanjeev Ganesh
santhiag@eng.ucsd.edu
Eamon Patmasing
epatamas@ucsd.edu
Dinesh Bharadia
dineshb@ucsd.edu
2023 IEEE 97th Vehicular Technology Conference (VTC2023-Spring)


Millimeter wave (mmWave) sensing has recently gained attention for its robustness in challenging environments. When visual sensors such as cameras fail to perform, mmWave radars can be used to provide reliable performance. However, the poor scattering performance and lack of texture in millimeter waves can make it difficult for radars to identify objects in some situations precisely. In this paper, we take insight from camera fiducials which are very easily identifiable by a camera, and present R-fiducial tags, which smartly augment the current infrastructure to enable myriad applications with mmwave radars. R-fiducial acts as fiducials for mmwave sensing, similar to camera fiducials, and can be reliably identified by a mmwave radar. We identify a set of requirements for millimeter wave fiducials and show how R-fiducial meets them all. R-fiducial uses a novel spread-spectrum modulation technique to provide low latency with high reliability. Our evaluations show that R-fiducial can be reliably detected with a 100% detection rate up to 25 meters with a 120-degree field of view and a few milliseconds of latency. We also conduct experiments and case studies in adverse and low visibility conditions to demonstrate the potential of R-fiducial in a variety of applications.



Website Template Originally made by Phillip Isola and Richard Zhang for colorful ECCV project; the code can be found here.