دانلود کتاب تخمین حالت سیستم‌های تعامل چندعاملی خودرو-جاده

Globally, the automotive industry is undergoing a profound transformation. The transition fromtraditional vehicles to intelligent vehicles, and fromhuman driving to autonomous driving, is fundamentally reliant on perception technologies that capture information about vehicles and roads. Information such as vehicle state and tire-road friction coefficient parameters is not only crucial for vehicle dynamics and control but also serves as core technologies in intelligent transportation systems and autonomous driving. With technological advancements, vehicle state estimation and tire-road friction coefficient identification have gradually transitioned from theoretical research to practical applications. This journey includes significant milestones such as estimating the motion state and tire-road friction coefficient of the host vehicle using onboard sensors and predicting the state of surrounding vehicles through vehicular communication networks. Additionally, the development of deep learning technologies has provided new solutions for acquiring this information. To provide a unified description of the states of various traffic elements, the authors have proposed the concept of the multi-agent vehicle-road interaction system (MVRIS). This concept describes a system composed of a vehicle and its surrounding traffic elements, distinguishing it from traditional macro-traffic research. In our book, the MVRIS consists of three main traffic elements: the host vehicle, the preceding vehicle, and the road. The authors have compiled their latest research work in this area into this book. The main topics discussed regarding the state estimation of the MVRIS include: 1) Ego-vehicle state estimation considering sensor data loss. 2) Ego-vehicle state estimation with unknown noise and parameter perturbations. 3) State estimation of the preceding vehicle with data loss and parameter Perturbations. 4) Tire-Road Friction Coefficient Estimation with parametersmismatch and data Loss. With the development of 5G communication technology, edge computing, and cloud computing,MVRIS state estimation technology is set to play a crucial role in a broader range of applications. In the future, vehicles will not merely be means of transportation but will serve as intelligent mobile terminals and centers for data collection and processing. This evolution places higher demands and challenges on MVRIS state estimation technology. We believe that, with continuous technological advancements, the estimation of MVRIS states will play an increasingly important role in intelligent transportation and autonomous driving. Professionals in the field of autonomous vehicles, as well as researchers, engineers, and scientists in related fields, can utilize State Estimation of Multi-Agent Vehicle-Road Interaction Systems to gain relevant knowledge. This book offers practical, precise, and validated algorithms that can be deployed in various real-world scenarios.