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Virginia Tech

Radar and LiDAR Fusion for Scaled Vehicle Sensing

Abstract

dc:description.abstract

Scaled test-beds (STBs) are popular tools to develop and physically test algorithms for advanced driving systems, but often lack automotive-grade radars in their sensor suites. To overcome resolution issues when using a radar at small scale, a high-level sensor fusion approach between the radar and automotive-grade LiDAR was proposed. The sensor fusion approach was expected to leverage the higher spatial resolution of the LiDAR effectively. First, multi object radar tracking software (RTS) was developed to track a maneuvering full-scale vehicle using an extended Kalman filter (EKF) and the joint probabilistic data association (JPDA). Second, a 1/5th scaled vehicle performed the same vehicle maneuvers but scaled to approximately 1/5th the distance and speed. When taking the scaling factor into consideration, the RTS' positional error at small scale was, on average, over 5 times higher than in the full-scale trials. Third, LiDAR object sensor tracks were generated for the small-scale trials using a Velodyne PUCK LiDAR, a simplified point cloud clustering algorithm, and a second EKF implementation. Lastly, the radar sensor tracks and LiDAR sensor tracks served as inputs to a high-level track-to-track fuser for the small-scale trials. The fusion software used a third EKF implementation to track fused objects between both sensors and demonstrated a 30% increase in positional accuracy for a majority of the small-scale trials when compared to using just the radar or just the LiDAR to track the vehicle. The proposed track fuser could be used to increase the accuracy of RTS algorithms when operating in small scale and allow STBs to better incorporate automotive radars into their sensor suites.

Degree

thesis:*
Name thesis:degree_name
Master of Science
Level thesis:degree_level
masters
Discipline thesis:degree_discipline
Engineering Mechanics
Department dc:contributor.department
Engineering Science and Mechanics
Grantor dc:publisher
Virginia Tech
Year dc:date.issued
2021

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Beale, Gregory Thomas
Chair dc:contributor.committeechair
  • Perez, Miguel A.
Committee members dc:contributor.committeemember
  • Doerzaph, Zachary R.
  • Berkmeier, Matthew D.

Subjects

dc:subject × 6

Rights

dc:rights
Statement dc:rights
  • In Copyright
Language dc:language.iso
en

Identifiers

dc:identifier.*
Dc Identifier Other
vt_gsexam:29485
OAI identifier oai:identifier
oai:vtechworks.lib.vt.edu:10919/102932

Chain of custody

source
Harvested from
Virginia Tech
Base URL
vtechworks.lib.vt.edu/oai/request
Last updated
2026-07-24
Source record
OAI-PMH GetRecord
related terms
citation

Beale, Gregory Thomas. Radar and LiDAR Fusion for Scaled Vehicle Sensing. masters thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/102932