As autonomous vehicles continue to grow in popularity, it is imperative for engineers to gain greater understanding of vehicle modeling and controls under different situations. Most research has been conducted on on-road ground vehicles, yet off-road ground vehicles which also serve vital roles in society have not enjoyed the same attention. The dynamics for off-road vehicles are far more complex due to different terrain conditions and 3D motion. Thus, modeling for control applications is difficult. A potential solution may be the incorporation of empirical data for modeling purposes, which is inspired by recent machine learning advances, but requires less computation. This thesis presents results for empirical modeling of an off-road ground vehicle, Polaris XP 900. As a first step, data was collected for 2D planar motion by obtaining several velocity step responses. Multivariable polynomial surface fits were performed for the step responses. Sliding mode control layered on top of pure pursuit guidance is then used to drive the vehicle for waypoint following, using the empirical model. Simulation and experimental results show that the vehicle can perform waypoint following for a circular and sinusoidal with minimal error. Furthermore, more experimental data was collected to show the effects of adaptive velocity and adaptive lookahead for path tracking. A comparison of the controller's performance was also explored between on-road and off-road terrain.

<div>While a majority of transportation and mobility solutions rely on in-vehicle sensors and the availability of the global positioning system (GPS) for absolute localization, alternate paradigms leveraging smart infrastructure have started becoming a viable solution for localization without needing GPS. However, the majority of approaches involving smart infrastructure require a means for wireless communication. In this article, we describe a novel method that can accurately localize the vehicle without using GPS and wireless communication by leveraging embedded digital and analog information on the roadside signage. The embedded information consists of a digital signature which can be used to cross-reference the ground truth (GT) location of the signage, as well as geometric information of the signage. This information is directly leveraged by on-vehicle sensors to generate absolute localization information. Specifically, the smart infrastructure consists of signage that is visible primarily in the infrared (IR) spectrum. A specialized camera that is optimized to read the digital signature extracts the analog information associated with the signage (ground truth and geometry). This is then used by both the camera, as well as a millimeter (mm)-wave radar to produce independent localization information. The camera and radar information are correlated with the signage information using a global nearest neighbor algorithm, followed by fusion with vehicle odometry using an extended Kalman filter (EKF) to generate accurate localization of the vehicle. The EKF is set up to manage asynchronous observations between the camera, radar, and vehicle odometry. The proposed method is implemented to localize a vehicle without the aid of GPS, and the results show consistent localization with the root mean squared (RMS) longitudinal and lateral errors less than 0.46 m and 0.19 m, respectively.</div>