| book details |

Recent Progress in State Estimation for the Vehicle Neighborhood System

By (author) Yan Wang, By (author) Guodong Yin, By (author) Chao Huang

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normal price: R 4,513.95

Price: R 4,061.95

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| book description |

Up-to-date discussions of the challenges and solutions in state estimation of vehicle neighborhood systems In Recent Progress in State Estimation for the Vehicle Neighborhood System, a team of distinguished researchers introduces a novel conceptual framework that defines a system comprising vehicles and local road segments within a connected vehicle (V2X) environmentâ€”referred to as the vehicle neighborhood system. Creative estimation methods for both states and parameters within this system have been proposed and potential applications of these methods have been discussed. The book places particular emphasis on estimating and analyzing the motion states of the ego vehicle and the preceding vehicle, as well as the tire road friction coefficient. The book covers a wide range of topics in the area of vehicle neighborhood systems, including sensor technologies, data fusion, filtering algorithms, engineering applications, and practical implementations of autonomous driving systems. It also explores common challenges in state and parameter estimation for related nonlinear systems, such as sensor data loss, unknown measurement noise, and model parameter perturbations. Corresponding solutions to these issues are proposed and discussed in detail. The book also includes: A thorough introduction to ego-vehicle state estimation with sensor data loss Comprehensive explorations of unknown noise and parameter perturbations in ego-vehicle state estimation Practical discussions of tire-road friction coefficient estimation with parameter mismatch and data loss Complete treatments of preceding vehicle state estimation Perfect for engineers and professionals with an interest in vehicle state estimation, Recent Progress in State Estimation for the Vehicle Neighborhood System will also benefit academics, scientists, and graduate students in areas like robotics, control systems, and autonomous systems.