AI-enabled Technologies for Autonomous and Connected Vehicles (Lecture Notes in Intelligent Transportation and Infrastructure)

AI-enabled Technologies for Autonomous and Connected Vehicles Description

This book covers the latest developments and research in the field of intelligent vehicle systems. With an emphasis on automated, autonomous, and networked vehicle systems, it presents a wide spectrum of AI-enabled technology. Advanced machine learning techniques are covered, including transfer learning, learning from huge data, deep learning, and reinforcement learning algorithms.

Control theory is also applied to vehicle and mobility systems. Additionally, it discusses the economical and environmental ramifications of automated mobility, as well as its human factors and energy-efficiency-related components. It also reflects on cutting-edge technology for environmental perception and vehicle-to-everything (V2X).

Here’s what you will learn in this book:

• Front Matter

Advances, Opportunities and Challenges in AI-enabled Technologies for Autonomous and Connected Vehicles

• Sensors and Perception
  • Semi-autonomous Truck Platooning with a Lean Sensor Package
  • Environmental Perception for Intelligent Vehicles
  • 3D Object Detection for Autonomous Driving
  • Comparative Study on Transfer Learning for Object Classification and Detection
  • Future Technology and Research Trends in Automotive Sensing
  • Automated Driving Decisions and Control
  • Robust AI Driving Strategy for Autonomous Vehicles
  • Artificially Intelligent Active Safety Systems
  • Model Predictive Control for Safe Autonomous Driving Applications
  • Energy-Efficient Autonomous Driving Using Cognitive Driver Behavioral Models and Reinforcement Learning
  • Self-learning Decision and Control for Highly Automated Vehicles

• Advanced Driver Assistant Systems
  • MAGMA: Mobility Analytics Generated from Metrics on ADAS
  • Driver Assistance Systems and Safety—Assessment and Challenges
  • Factors Influencing Driver Behavior and Advances in Monitoring Methods
• Connected Autonomous Vehicles, Mobility, and Security
  • Towards Learning-Based Control of Connected and Automated Vehicles: Challenges and Perspectives