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Reinforcement Learning for Sequential Decision and Optimal Control
Have you ever wondered how AlphaZero learns to defeat the top human Go players? Do you have any clues about how an autonomous driving system can gradually develop self-driving skills beyond normal drivers? What is the key that enables AlphaStar to make decisions in Starcraft, a notoriously difficult strategy game that has partial information and complex rules? The core mechanism underlying those recent technical breakthroughs is reinforcement learning (RL), a theory that can help an agent to develop the self-evolution ability through continuing environment interactions. In the past few years, the AI community has witnessed phenomenal success of reinforcement learning in various fields, inclu...
Automotive Air Conditioning
This book presents research advances in automotive AC systems using an interdisciplinary approach combining both thermal science, and automotive engineering. It covers a variety of topics, such as: control strategies, optimization algorithms, and diagnosis schemes developed for when automotive air condition systems interact with powertrain dynamics. In contrast to the rapid advances in the fields of building HVAC and automotive separately, an interdisciplinary examination of both areas has long been neglected. The content presented in this book not only reveals opportunities when interaction between on-board HVAC and powertrain is considered, but also provides new findings to achieve performance improvement using model-based methodologies.
Distributional Reinforcement Learning
The first comprehensive guide to distributional reinforcement learning, providing a new mathematical formalism for thinking about decisions from a probabilistic perspective. Distributional reinforcement learning is a new mathematical formalism for thinking about decisions. Going beyond the common approach to reinforcement learning and expected values, it focuses on the total reward or return obtained as a consequence of an agent's choices—specifically, how this return behaves from a probabilistic perspective. In this first comprehensive guide to distributional reinforcement learning, Marc G. Bellemare, Will Dabney, and Mark Rowland, who spearheaded development of the field, present its key...
Robust Environmental Perception and Reliability Control for Intelligent Vehicles
This book presents the most recent state-of-the-art algorithms on robust environmental perception and reliability control for intelligent vehicle systems. By integrating object detection, semantic segmentation, trajectory prediction, multi-object tracking, multi-sensor fusion, and reliability control in a systematic way, this book is aimed at guaranteeing that intelligent vehicles can run safely in complex road traffic scenes. Adopts the multi-sensor data fusion-based neural networks to environmental perception fault tolerance algorithms, solving the problem of perception reliability when some sensors fail by using data redundancy. Presents the camera-based monocular approach to implement th...
Explainable Artificial Intelligence for Intelligent Transportation Systems
Artificial Intelligence (AI) and Machine Learning (ML) are set to revolutionize all industries, and the Intelligent Transportation Systems (ITS) field is no exception. While ML, especially deep learning models, achieve great performance in terms of accuracy, the outcomes provided are not amenable to human scrutiny and can hardly be explained. This can be very problematic, especially for systems of a safety-critical nature such as transportation systems. Explainable AI (XAI) methods have been proposed to tackle this issue by producing human interpretable representations of machine learning models while maintaining performance. These methods hold the potential to increase public acceptance and trust in AI-based ITS. FEATURES: Provides the necessary background for newcomers to the field (both academics and interested practitioners) Presents a timely snapshot of explainable and interpretable models in ITS applications Discusses ethical, societal, and legal implications of adopting XAI in the context of ITS Identifies future research directions and open problems
Model-Based Control Engineering
Progress in industrialization and automation engineering is creating many new opportunities in the autonomous systems industry. With the uncertain and highly nonlinear dynamics of the real world where these new technologies will be deployed, a reliable control strategy is necessary. This book provides a high-level discussion on model-based control engineering and its various applications.
Nonlinear Control Technology of Vehicle Chassis-by-Wire System
This book belongs to the field of intelligent vehicle control, which is dedicated to the research of nonlinear control problems of intelligent vehicle chassis-by-wire systems. Through the nonlinear stability control of the steer-by-wire system and the consistency optimization control of the brake-by-wire system, the performance of the vehicle subsystem is improved. Then, the decoupling control of the nonlinear inverse system is used to realize the decoupling of the chassis-by-wire system. Finally, this book further adopts nonlinear rollover prevention integrated control to improve the rollover prevention performance of the vehicle.
Autonomous Vehicle
Autonomous vehicles, despite their relatively short history, have already found practical application in many areas of human activity. Such vehicles are usually replacing people in performing tasks that require long operating time and are held in inaccessible or hazardous environments. Nevertheless, autonomous robotics is probably the area that is being developed the most because of the great demand for such devices in different areas of our lives. This book is a collection of experiences shared by scientists from different parts of the world doing researches and daily exploiting autonomous systems. Giving this book in the hands of the reader, we hope that it will be a treasure trove of knowledge and inspiration for further research in the field of autonomous vehicles.
Human-Like Decision Making and Control for Autonomous Driving
This book details cutting-edge research into human-like driving technology, utilising game theory to better suit a human and machine hybrid driving environment. Covering feature identification and modelling of human driving behaviours, the book explains how to design an algorithm for decision making and control of autonomous vehicles in complex scenarios. Beginning with a review of current research in the field, the book uses this as a springboard from which to present a new theory of human-like driving framework for autonomous vehicles. Chapters cover system models of decision making and control, driving safety, riding comfort and travel efficiency. Throughout the book, game theory is appli...
