Construction Methods For An Autonomous Driving Map In An Intelligent Network Environment

This book provides an overview of constructing advanced Autonomous Driving Maps. It includes coverage of such methods as: fusion target perception (based on vehicle vision and millimeter wave radar), cross-field of view object perception, vehicle motion recognition (based on vehicle road fusion information), vehicle trajectory prediction (based on improved hybrid neural network) and the driving map construction method driven by road perception fusion. An Autonomous Driving Map is used for optimization of not only for a single vehicle, but also for the entire traffic system.

This book elaborates the fundamentals, new concepts and key technologies of the Intelligent Environment Friendly Vehicle (i-EFV), and the engineering implementation of these technologies such as structure sharing, data fusion and control coordination. With lots of illustrations, it summaries the authors’ research in the field of automotive intelligent technology and electric vehicle control for the past twenty years, enabling readers to grasp the essence of automotive power revolution, intelligent revolution and information revolution. Opening up new scientific horizons and fostering innovative thinking, the book is a valuable resource for researchers as well as undergraduate and graduate students.

This book describes an effective decision-making and planning architecture for enhancing the navigation capabilities of automated vehicles in the presence of non-detailed, open-source maps. The system involves dynamically obtaining road corridors from map information and utilizing a camera-based lane detection system to update and enhance the navigable space in order to address the issues of intrinsic uncertainty and low-fidelity. An efficient and human-like local planner then determines, within a probabilistic framework, a safe motion trajectory, ensuring the continuity of the path curvature and limiting longitudinal and lateral accelerations. LiDAR-based perception is then used to identify the driving scenario, and subsequently re-plan the trajectory, leading in some cases to adjustment of the high-level route to reach the given destination. The method has been validated through extensive theoretical and experimental analyses, which are reported here in detail.

This comprehensive handbook covers Geospatial Artificial Intelligence (GeoAI), which is the integration of geospatial studies and AI machine (deep) learning and knowledge graph technologies. It explains key fundamental concepts, methods, models, and technologies of GeoAI, and discusses the recent advances, research tools, and applications that range from environmental observation and social sensing to natural disaster responses. As the first single volume on this fast-emerging domain, Handbook of Geospatial Artificial Intelligence is an excellent resource for educators, students, researchers, and practitioners utilizing GeoAI in fields such as information science, environment and natural resources, geosciences, and geography. Features Provides systematic introductions and discussions of GeoAI theory, methods, technologies, applications, and future perspectives Covers a wide range of GeoAI applications and case studies in practice Offers supplementary materials such as data, programming code, tools, and case studies Discusses the recent developments of GeoAI methods and tools Includes contributions written by top experts in cutting-edge GeoAI topics This book is intended for upper-level undergraduate and graduate students from different disciplines and those taking GIS courses in geography or computer sciences as well as software engineers, geospatial industry engineers, GIS professionals in non-governmental organizations, and federal/state agencies who use GIS and want to learn more about GeoAI advances and applications.

The subject of this book is artificial intelligence (AI), introducing the fast road sensing algorithm and system based on image pattern recognition for unmanned vehicle, especially for traffic sign recognition and complex road recognition. With rich figures and credible data, this book systematically and comprehensively describes the core technology and industrialization focus of today's unmanned vehicle system, which can be used as a reference for R & D Engineers and industrialization practitioners of unmanned vehicle, and it can also be used as a teaching material for higher grades and postgraduates in colleges and universities.

This two-volume set of CCIS 391 and CCIS 392 constitutes the refereed proceedings of the Fourth International Conference on Information Computing and Applications, ICICA 2013, held in Singapore, in August 2013. The 126 revised full papers presented in both volumes were carefully reviewed and selected from 665 submissions. The papers are organized in topical sections on Internet computing and applications; engineering management and applications; intelligent computing and applications; control engineering and applications; cloud and evolutionary computing; knowledge management and applications; computational statistics and applications.

"This book argues that computational models in behavioral neuroscience must be taken with caution, and advocates for the study of mathematical models of existing theories as complementary to neuro-psychological models and computational models"--

This book gathers selected papers presented at the 4th International Conference on Wireless Communications and Applications (ICWCA 2020), held at Hainan University, China. The second volume will involve research works aimed at the contemporary applications: emerging wireless/mobile applications, context and location-aware wireless services; wireless telemedicine and e-health services; intelligent transportation systems; RFID technology and application; cognitive radio and sensor-based applications; content distribution in wireless home environment and many others.