This project is concerned with accurately and reliably determining the state of a vehicle relative to a specified trajectory (e.g., a lane centerline). We are utilizing inertial navigation methods based on inexpensive solid state inertial sensors aided by external sensors such as carrier phase differential GPS, magnetometers, and roadway height. Due to this integration of sensors, reliability is increased relative to a single sensor approach and the changes required to the roadway infrastructure may be significantly decreased. This projects objectives included preparation for and participation in DEMO2002, research into INS aided GPS tracking of satellites, and research into methods to use auxiliary sensors (roadway height or magnetometers) to aid the integer resolution process. This project has been very interesting and successful. Although Demo2002 was ultimately cancelled our preparations for it were fruitful and we did participate in a smaller project demonstration with PATH at Crows Landing. In preparing for participation in Demo2002, we constructed much more robust prototypes of our GPS aided INS hardware. This include significant software re-writing that improved the reliability of the software. Overall, the hardware and software are now much more reliable and easier to use and install on test vehicles. In the area of INS aiding of a GPS receiver, research was performed and a new algorithm was design, but not implemented. We expect that this approach would lead to better satellite tracking during brief interruptions of the satellite signal. Implementation would require extensive interactions with the receiver design team, which is no longer possible due to changes in the GPS industry. In the area of aided integer ambiguity resolution, we have developed new algorithms to use information from non-GPS sensors to facilitate and speed-up the process of integer ambiguity resolution. This algorithm was evaluated in two experiments at UCR with significant improvements in the ability to correctly identify the integer ambiguities in a single epoch with fewer than 6 satellites. This algorithm was also used during the Crows Landing testing. Finally, UCR and PATH worked at PATH and Crows Landing and generated an impressive set of data as shown in Figures 12-25 of Section 4.2. This set of experiments was the culmination of the project. The experiments mounted the GPS/INS hardware on a bus that already was instrumented with the magnetometer hardware. We demonstrated that (1) the INS control state and magnetometer control state matched very well both when only the DCPGPS is aiding the INS and when both the magnetometer and DCPGPS are aiding the INS; (2) seamless transitions between magnetometer control, INS control, and manual control; (3) advanced maneuvers such as lane changes.

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## Scholarly Works (49 results)

Reliable and high accuracy (decimeter level) localization of a rover relative to a defined frame is an enabling technology for numerous Intelligent Transportation Systems (ITS) applications (e.g., automotive guidance, routing, lane departure warning). The goal of localization is to compute the navigation state of the rover in some defined frame of reference such that the expected errors in the estimate is within a given performance specification.

Inertial navigation is a popular navigation technique since it provides full six Degree-Of-Freedom (DOF) navigation information. Further inertial sensors have been studied for decades and have well understood error models. This dissertation discusses the theoretical and implementation aspects of certain sensor aided Inertial Navigation Systems (INS). Though the presentation can be easily generalized to all forms of INS, the primary focus of this dissertation will be on automotive INS.

This dissertation formulates the localization problem in a mathematically rigorous fashion and poses it as a nonlinear Bayesian estimation problem. The INS kinematic equations and linearized error state equations required by the Bayesian estimation solution are derived. Aiding techniques like GPS, Vision and stationary aiding are described and mathematically formulated. Observability and performance analysis are presented for each of these aiding scenarios. The last part of the dissertation defines and formulates the Near Real Time estimation problem.

This report describes the results of an effort to implement and analyze the performance a vehicle control system using control state information obtained from a carrier phase Differential Global Positioning System (DGPS) aided Inertial Navigation System (INS). Keywords: Vehicle Positioning Systems, Global Positioning System, Inertial Navigation, Differential Carrier Phase, Advanced Vehicle Control

Vehicle formation control is one of important research topics in transportation. Control of uncertain nonlinear systems is one of fundamental problems in vehicle control. In this dissertation, we consider this fundamental control problem. Specially, we considered self-organizing based tracking control of uncertain nonaffine systems and optimal control of uncertain nonlinear systems. In tracking control of nonaffine systems, a self-organizing on-line approximation based controller is proposed to achieve a

prespecified tracking accuracy, without using high-gain control nor large magnitude switching. For optimal control of uncertain nonlinear systems, we considered point-wise min-norm optimal control of uncertain nonlinear systems and approximately optimal control of uncertain nonlinear systems. In point-wise non-norm optimal control, optimal regulation and optimal tracking controllers were proposed with the aid of locally weighted learning observers. By introducing control Lyapunov functions and redefining the optimal criterions, analytic controllers were proposed and were optimal in the sense of min-norm. In approximately optimal control of uncertain nonlinear systems, adaptive optimal controllers were proposed with the aid of iterative approximation techniques and adaptive control. By iteratively learning, the difficulty of solving Hamilton-Jacobian-Bellman (HJB) equation is overcome. The proposed adaptive optimal algorithms can be applied to solve optimal control problem of a large class of nonlinear systems. To show effectiveness of the proposed controllers for above problems, simulations were done in computers.

This paper presents a novel method to utilize\textit{2D} LIDAR for INS (Inertial Navigation System) aiding to improve\textit{3D} vehicle position estimation accuracy, especially when GNSS signals are shadowed.In the proposed framework, 2D LIDAR aiding is carried out without imposing any assumptions on the vehicle motion (e.g. we allow full six degree-of freedom motion).To achieve this, a closed-form formula is derived to predict the line measurement in the LIDAR's frame.This makes the feature association, residual formation and GUI display possible.With this formula, the Extended Kalman Filter (EKF) can be employed in a straightforward manner to fuse the LIDAR and IMU data to estimate the full state of the vehicle.

Preliminary experimental results show the effectiveness of the LIDAR aiding in reducing the state estimation uncertainty along certain directions, when GNSS signals are shadowed.