Date of Award

2020

Document Type

Open Access Dissertation

Degree Name

Doctor of Philosophy in Electrical Engineering (PhD)

Administrative Home Department

Department of Electrical and Computer Engineering

Advisor 1

Daniel R. Fuhrmann

Committee Member 1

Saeid Nooshabadi

Committee Member 2

Timothy J. Schulz

Committee Member 3

Timothy C. Havens

Committee Member 4

Allan A. Struthers

Abstract

Multiple sensors are increasingly being deployed on systems for perception applications. In particular, vehicles are becoming equipped with a suite of sensors for advanced driver assistance features and autonomous driving. This dissertation considers automotive radar sensors mounted at the rear of a vehicle with the main objective of using their point cloud detections to estimate the rotation of a trailer which is attached to the vehicle's hitch ball. A simulation-based study of the problem is presented first. Thereafter, the problem is considered with respect to experimental radar data collected in both indoor and outdoor environments; the environmental difference is in the roughness of the ground surfaces. The apparatus used for the data collection includes two radars, which provide point detections in two dimensions -- range and azimuth, installed in the tail light fixtures of a truck. The estimation algorithm, based on the experimental data, includes the fusion of radar detections onto a coordinate system centered at the hitch ball position, a rotational point set registration algorithm, constrained orthogonal Procrustes optimization, and state estimation with the Kalman filter to obtain smooth estimates of the trailer rotation angle. In one implementation of the estimation algorithm, the dimensions of the radar geometry, which are required in its radar fusion procedure, are obtained by direct measurement. In another implementation, the calibration of the radar geometry is considered; two extrinsic calibration methods which estimate the dimensions of the geometry using the radar detections are provided. The trailer angle estimation algorithm is then used with respect to the calibration parameters. The results presented show that the trailer angle estimates obtained with respect to a direct measurement of the radar geometry parameters are comparable with those obtained with respect to the calibration parameters and that the algorithms presented for trailer angle estimation and extrinsic radar calibration are feasible for deployment. It is also shown that the trailer angle estimation algorithm has improved performance with the indoor dataset than with the outdoor dataset. The challenges observed with the outdoor dataset are presented and recommended for future research.

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