Beale, Gregory T.Berkemeier, Matthew D.Doerzaph, Zachary R.Perez, Miguel A.2022-07-142022-07-142022-05http://hdl.handle.net/10919/111249Scaled test beds are popular tools to develop and physically test algorithms for advanced driving systems, but they often lack automotive-grade radars in their sensor suites. To overcome resolution issues when using a radar at small scale, a high-level radar and automotive-grade LiDAR sensor fusion approach that effectively leveraged the higher spatial resolution of LiDAR was proposed. First, radar tracking software (RTS) was developed to track a maneuvering full-scale vehicle using an extended Kalman filter (EKF) and a popular data association technique. Second, a 1/5th scaled vehicle performed the same vehicle maneuvers but scaled to approximately 1/5th the distance and speed. When considering the scaling factor, the RTS’s positional error at small scale was over 5 times higher on average than in the full-scale trials. Third, LiDAR object tracks were generated for the small-scale trials using a second EKF implementation and then combined with the radar objects in a high-level track fusion algorithm. The fused tracks demonstrated a 30% increase in positional accuracy for a majority of the small-scale trials when compared to tracks using just the radar or just the LiDAR. The proposed track fuser could allow scaled test beds to incorporate automotive radars into their sensor suites more effectively by augmenting the radar output with LiDAR, overcoming the resolution issues that afflict radar when operating at small scale.application/pdfenCC0 1.0 Universalradar trackingLiDAR trackingKalman filtertrack-to-track fusionobject trackingsensor fusionscaled test bedReport