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Construction Methods for an Autonomous Driving Map in an Intelligent Network Environment

  • 1st Edition - April 4, 2024
  • Latest edition
  • Author: Zhijun Chen
  • Language: English

Construction Methods for an Autonomous Driving Map in an Intelligent Network Environment not only supports the development of Intelligent & Connected Transportation, but also… Read more

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Description

Construction Methods for an Autonomous Driving Map in an Intelligent Network Environment not only supports the development of Intelligent & Connected Transportation, but also promotes the landing application of autonomous driving. Areas covered include the fusion target perception method based on vehicle vision and millimeter wave radar, cross-field of view object perception method, vehicle motion recognition method based on vehicle road fusion information, vehicle trajectory prediction method based on improved hybrid neural network and driving map construction driven by road perception fusion are introduced in this book.

Benefiting from the development of computer technique, the advanced machine learning and artificial intelligence theories are used by this book to show readers the construction process of the Autonomous Driving Map.

Key features

  • Delivers an Autonomous Driving Map to provide safer and more effective autonomous driving travel services for travelers of different travel modes and technical levels, optimized not only for a single vehicle, but also for an entire traffic system
  • Provides an advanced Autonomous Driving Map construction method, which can help promote the development of Intelligent & Connected Transportation System
  • Presents advanced machine learning and artificial intelligence theories used to solve some important problems in the field of autonomous driving

Readership

Researchers involved in autonomous driving, traffic planning, traffic engineering, traffic control and traffic management

Table of contents

1. Introduction

2. Fusion Target Perception Method Based on Vehicle Vision and Millimeter Wave Radar

3. Cross-Field of View Object Perception Method

4. Vehicle Motion Recognition Method Based on Vehicle Road Fusion Information

5. Vehicle Trajectory Prediction Method Based on Improved Hybrid Neural Network

6. Driving Map Construction Driven by Road Perception Fusion

7. Summary and conclusions

Product details

  • Edition: 1
  • Latest edition
  • Published: April 4, 2024
  • Language: English

About the author

ZC

Zhijun Chen

Dr Chen is the Deputy Director of the Institute of Traffic Information and Intelligent Systems, Intelligent Transportation Systems Research Center, Wuhan University of Technology. His expertise areas include artificial intelligence, image processing, big data mining, vehicle-road collaboration and connected automated driving, intelligent driving, autonomous driving
Affiliations and expertise
Deputy Director, Institute of Traffic Information and Intelligent Systems, Intelligent Transportation Systems Research Center, Wuhan University of Technology, China

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