Construction Methods for an Autonomous Driving Map in an Intelligent Network Environment 1st Edition

Grab attention with cutting-edge knowledge that bridges mapping science and connected mobility: Construction Methods for an Autonomous Driving Map in an Intelligent Network Environment, 1st Edition by Zhijun Chen delivers a practical, research-informed guide for today’s smart-transport challenges.

Discover how to design, build, and maintain the high-definition geospatial foundations that autonomous systems depend on. This book unpacks robust methodologies for map data acquisition, sensor fusion (LiDAR, camera, radar), georeferencing, semantic annotation, and change detection—framed specifically for an intelligent network environment that includes V2X, edge computing, and 5G-enabled real-time updates. Clear explanations of mapping pipelines, data quality assurance, and localization strategies make complex concepts accessible to engineers, researchers, and planners.

Every chapter connects theory with application: algorithmic workflows, case studies from urban deployments, best practices for scalable map databases, and techniques for ensuring resilience in dynamic road networks. Whether you work in autonomous vehicle development, smart-city planning, or geospatial services across Asia, Europe, or the Americas, the book’s actionable insights help you reduce development risk and accelerate deployment.

Imagine faster, safer lane-level localization, smoother sensor integration, and maps that stay current in live networked environments—this title equips teams to make that a reality. Well-suited for professionals and advanced students, Zhijun Chen’s first edition is a go-to reference for anyone building the maps behind tomorrow’s automated mobility.

Order your copy today to bring rigorous, field-tested mapping methods into your autonomous driving projects and smart mobility initiatives.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.