The primary focus of this work is to develop a vehicle state estimation algorithm using a-priori knowledge of the environment. Specifically, this work focuses on the problem of achieving accurate localization of a vehicle within a map and on the road, using a map as a feedforward sensor to help estimate the location of the vehicle using image features. Presented here is a method for improving localization over standard GPS and inertial-based methods via map-based, monocular vision, state estimation algorithms. The measurements obtained from a camera pose estimation algorithm are fused with a dynamic vehicle model to improve vehicle state estimation in a real-time implementable algorithm. The presented methods, utilizing kinematic and dynamic modeling, allow for the calculation of the influence of specific three-dimensional road features when measuring a vehicle's pose. Additionally, the combined simulation and experimental implementation of these methods enabled comparative evaluations of the bounded region wherein the pose estimator can converge to the true vehicle pose under common road scenes using a map of the lane marker features. Finally, this work examines the use of a map-based Kalman filtering method using previewed road features and vehicle steering inputs, in coordination with the image-based pose estimation, to further improve the vehicle's state estimate.

This book offers a snapshot of the latest research and developments in road and railway vehicle dynamics. Gathering peer-reviewed contributions to the 27th Symposium of the International Association of Vehicle System Dynamics (IAVSD), held online on August 17–19, 2021, from Saint Petersburg, Russia, it offers extensive information for both researchers and professionals in the field of ground vehicle dynamics, control and design. It covers cutting-edge methods and solutions for solving ground vehicle system dynamics-related problems, concerning control and monitoring, performance, safety and braking of road and rail vehicles, including electric and autonomous ones. Further, it reports on significant advances in vehicle design, and important applications to improve ride comfort. Overall, the book provides academics and professional with a timely reference guide on theories and methods to understand, analyze and improve vehicle stability and dynamics in a broad range of different operating conditions. Chapter "Experimental Validation of a Semi-physical Modelling Approach of the Influence of Tyre Rotation on the Vertical Tyre Force Transmission and Tyre Kinematics" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.

This thesis presents a novel state estimation structure, a hybrid extended Kalman filter/Kalman filter developed for a skid-steered, six-wheeled, ARGO® all-terrain vehicle (ATV). The ARGO ATV is a teleoperated unmanned ground vehicle (UGV) custom fitted with an inertial measurement unit, wheel encoders and a GPS. In order to enable the ARGO for autonomous applications, the proposed hybrid EKF/KF state estimator strategy is combined with the vehicle's sensor measurements to estimate key parameters for the vehicle. Field experiments in this thesis reveal that the proposed estimation structure is able to estimate the position, velocity, orientation, and longitudinal slip of the ARGO with a reasonable amount of accuracy. In addition, the proposed estimation structure is well-suited for online applications and can incorporate offline virtual GPS data to further improve the accuracy of the position estimates. The proposed estimation structure is also capable of estimating the longitudinal slip for every wheel of the ARGO, and the slip results align well with the motion estimate findings.

This book tackles some of the most challenging problems in state estimation and traction coordinated control systems to improve the dynamic control performance of Distributed Electric Vehicles. The developed methods make it possible to gain more accurate information regarding the vehicle states, ensure more desirable vehicle motions and better robustness in unforeseeable driving environments. Given the impressive features of Distributed Electric Vehicles, including their simple and compact structure, short transmission chains, fast and accurate control response, modular drivetrain design etc., it is widely recognized that they represent an important future development direction and attract many of the brightest engineers and scientists. This book makes a significant contribution to the design of safer and more efficient vehicles.

Information engineering and applications is the field of study concerned with constructing information computing, intelligent systems, mathematical models, numerical solution techniques, and using computers and other electronic devices to analyze and solve natural scientific, social scientific and engineering problems. Information engineering is an important underpinning for techniques used in information and computational science and there are many unresolved problems worth studying. The Proceedings of the 2nd International Conference on Information Engineering and Applications (IEA 2012), which was held in Chongqing, China, from October 26-28, 2012, discusses the most innovative research and developments including technical challenges and social, legal, political, and economic issues. A forum for engineers and scientists in academia, industry, and government, the Proceedings of the 2nd International Conference on Information Engineering and Applications presents ideas, results, works in progress, and experience in all aspects of information engineering and applications.

The work provides novel methods to process inertial sensor and acoustic sensor data for road condition estimation and monitoring with application in vehicles, which serve as sensor platforms. Furthermore, methods are introduced to combine the results from various vehicles for a more reliable estimation.