Control, Learning and Optimization with Applications in Connected and Autonomous Vehicles (Transportation)

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予約

Control, Learning and Optimization with Applications in Connected and Autonomous Vehicles (Transportation)

Gao, Weinan (EDT)/ Jiang, Zhong-Ping (EDT)/ Malikopoulos, Andreas A. (EDT)

ウェブストア価格 ¥33,792（本体¥30,720）
Institution of Engineering and Technology（2026/06発売）
外貨定価 US$ 160.00
ポイント 307pt

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製本 Hardcover:ハードカバー版／ページ数 398 p.
言語 ENG
商品コード 9781837241606
DDC分類 629.046

Full Description

Connected and autonomous vehicles (CAVs) have enormous potential to shape the future of transportation. As this complex and dynamic field grows, researchers are looking for ways to improve the efficiency and performance of CAVs. Through employing predictive modeling, machine learning, and advanced sensor fusion approaches, CAVs can anticipate and respond to hazardous situations with greater precision and speed. Control algorithms coupled with real-time data analysis enable CAVs to achieve significant reductions in energy consumption without compromising performance or safety.

This book investigates the convergence of control, learning, and optimization techniques used to enhance CAV safety, mobility, energy efficiency, and overall performance, helping readers gain a deeper understanding of the key developments and emerging trends in CAV technologies.

It includes chapters on human-vehicle shared control, vehicle platooning, motion prediction and planning for autonomous vehicles, predictive and adaptive cruise control, reinforcement learning, energy optimisation, as well as cyber-security and privacy issues in learning-based vehicle control.

This book is a comprehensive resource for researchers and advanced students interested in the transformative potential of CAVs in future transport and looking for further insights to navigate this complex and dynamic field.

Chapter 1: Introduction
Chapter 2: Human-Vehicle Shared Control for Highly Automated Vehicles
Chapter 3: Mesoscopic Control of Traffic with Mixed Autonomy: Sequencing, Platooning, and Routing
Chapter 4: Dissipative Barrier Feedback for Collision Avoidance in Vehicle Platooning
Chapter 5: Privacy-Conscious Data-Enabled Predictive Leading Cruise Control via Affine Masking
Chapter 6: Highway Platoon Merging Control using RL: A Review
Chapter 7: Advances in Motion Prediction and Planning for Autonomous Vehicles: From Classical Methods to Modern AI-Based Approaches
Chapter 8: Data-Driven Predictive Cruise Control and Cooperative Adaptive Cruise Control for Connected and Autonomous Vehicles based on Reinforcement Learning
Chapter 9: Cyber-Resilient Learning-Based Controller Design for Adaptive Cruise Control
Chapter 10: Hierarchical Framework of Network-Level Routing and Trajectory Planning for Emerging Mobility Systems
Chapter 11: Safe Interactions Between Autonomous and Human-Driven Vehicles with Cooperation Compliance for Social Optimality
Chapter 12: Real-time Energy Optimization Approaches for Connected and Automated Hybrid Electric Vehicle
Chapter 13: Stochastic Energy Management Strategies for Connected Hybrid Electric Vehicles