Peng Dong

Over seven detail-rich chapters, this book comprehensively describes autonomous vehicle chassis modeling and control, chassis domain dynamic control, the estimation of essential dynamic states, research on motion planning, the development of chassis coordinated control, and related topics. This book first summarizes vehicle dynamic modeling and control and provides the background and related topics for chassis domain dynamic control. Following this, the author presents the motivations of chassis domain control and introduces its conceptual framework. The book then focuses on the identification of tire-road interactions, which contain lateral, longitudinal, and vertical tire forces, before then discussing the estimation of essential dynamic states, which represent vehicle handling stability status, and the observation of road surface coefficient. The quantitative evaluation of vehicle chassis domain performance is then provided, with the rigorous definition and design of a comprehensive metric for assessing chassis dynamic performance. Next, the book instructs readers on the chassis-domain dynamic-aware motion planning for autonomous vehicles and the multi-objective multi-subsystem coordinated control. Finally, the author presents the conclusions and future recommendations for the advanced control of autonomous vehicles. The content and structure of this book will enable readers to address the high complexity and unpredictability of traffic conditions, along with the strong nonlinearity of vehicle dynamics during maneuvers to facilitate the safe and coordinated operations of chassis subsystems. This will further the advancement of autonomous vehicles as the automobile industry transitions into the intelligent age. This is a vital guide for readers from various expertise backgrounds. Advanced undergraduate and postgraduate students who study vehicle engineering will benefit from the descriptions of theoretical foundations and practical methodologies. Engineers and researchers will also benefit from the unique insight into modeling and control technologies for autonomous vehicles.

This book gives a concise and comprehensive overview of non-cooperative target tracking, fusion and control. Focusing on algorithms rather than theories for non-cooperative targets including air and space-borne targets, this work explores a number of advanced techniques, including Gaussian mixture cardinalized probability hypothesis density (CPHD) filter, optimization on manifold, construction of filter banks and tight frames, structured sparse representation, and others. Containing a variety of illustrative and computational examples, Non-cooperative Target Tracking, Fusion and Control will be useful for students as well as engineers with an interest in information fusion, aerospace applications, radar data processing and remote sensing.

This book gives a concise and comprehensive overview of non-cooperative target tracking, fusion and control. Focusing on algorithms rather than theories for non-cooperative targets including air and space-borne targets, this work explores a number of advanced techniques, including Gaussian mixture cardinalized probability hypothesis density (CPHD) filter, optimization on manifold, construction of filter banks and tight frames, structured sparse representation, and others. Containing a variety of illustrative and computational examples, Non-cooperative Target Tracking, Fusion and Control will be useful for students as well as engineers with an interest in information fusion, aerospace applications, radar data processing and remote sensing.