Share

Decision Making, Planning, and Control Strategies for Intelligent Vehicles

2022-05-31 Technology & Engineering
Download Decision Making, Planning, and Control Strategies for Intelligent Vehicles PDF Online Free

Author :
Release : 2022-05-31
Genre : Technology & Engineering
Kind : eBook
Book Rating : 061/5 ( reviews)

GET EBOOK


Book Synopsis Decision Making, Planning, and Control Strategies for Intelligent Vehicles by : Haotian Cao

Download or read book Decision Making, Planning, and Control Strategies for Intelligent Vehicles written by Haotian Cao. This book was released on 2022-05-31. Available in PDF, EPUB and Kindle. Book excerpt: The intelligent vehicle will play a crucial and essential role in the development of the future intelligent transportation system, which is developing toward the connected driving environment, ultimate driving safety, and comforts, as well as green efficiency. While the decision making, planning, and control are extremely vital components of the intelligent vehicle, these modules act as a bridge, connecting the subsystem of the environmental perception and the bottom-level control execution of the vehicle as well. This short book covers various strategies of designing the decision making, trajectory planning, and tracking control, as well as share driving, of the human-automation to adapt to different levels of the automated driving system. More specifically, we introduce an end-to-end decision-making module based on the deep Q-learning, and improved path-planning methods based on artificial potentials and elastic bands which are designed for obstacle avoidance. Then, the optimal method based on the convex optimization and the natural cubic spline is presented. As for the speed planning, planning methods based on the multi-object optimization and high-order polynomials, and a method with convex optimization and natural cubic splines, are proposed for the non-vehicle-following scenario (e.g., free driving, lane change, obstacle avoidance), while the planning method based on vehicle-following kinematics and the model predictive control (MPC) is adopted for the car-following scenario. We introduce two robust tracking methods for the trajectory following. The first one, based on nonlinear vehicle longitudinal or path-preview dynamic systems, utilizes the adaptive sliding mode control (SMC) law which can compensate for uncertainties to follow the speed or path profiles. The second one is based on the five-degrees-of-freedom nonlinear vehicle dynamical system that utilizes the linearized time-varying MPC to track the speed and path profile simultaneously. Toward human-automation cooperative driving systems, we introduce two control strategies to address the control authority and conflict management problems between the human driver and the automated driving systems. Driving safety field and game theory are utilized to propose a game-based strategy, which is used to deal with path conflicts during obstacle avoidance. Driver's driving intention, situation assessment, and performance index are employed for the development of the fuzzy-based strategy. Multiple case studies and demos are included in each chapter to show the effectiveness of the proposed approach. We sincerely hope the contents of this short book provide certain theoretical guidance and technical supports for the development of intelligent vehicle technology.

Decision-Making Techniques for Autonomous Vehicles

2023-03-03 Technology & Engineering
Download Decision-Making Techniques for Autonomous Vehicles PDF Online Free

Author :
Release : 2023-03-03
Genre : Technology & Engineering
Kind : eBook
Book Rating : 491/5 ( reviews)

GET EBOOK


Book Synopsis Decision-Making Techniques for Autonomous Vehicles by : Jorge Villagra

Download or read book Decision-Making Techniques for Autonomous Vehicles written by Jorge Villagra. This book was released on 2023-03-03. Available in PDF, EPUB and Kindle. Book excerpt: Decision-Making Techniques for Autonomous Vehicles provides a general overview of control and decision-making tools that could be used in autonomous vehicles. Motion prediction and planning tools are presented, along with the use of machine learning and adaptability to improve performance of algorithms in real scenarios. The book then examines how driver monitoring and behavior analysis are used produce comprehensive and predictable reactions in automated vehicles. The book ultimately covers regulatory and ethical issues to consider for implementing correct and robust decision-making. This book is for researchers as well as Masters and PhD students working with autonomous vehicles and decision algorithms. Provides a complete overview of decision-making and control techniques for autonomous vehicles Includes technical, physical, and mathematical explanations to provide knowledge for implementation of tools Features machine learning to improve performance of decision-making algorithms Shows how regulations and ethics influence the development and implementation of these algorithms in real scenarios

Human-Like Decision Making and Control for Autonomous Driving

2022-07-25 Mathematics
Download Human-Like Decision Making and Control for Autonomous Driving PDF Online Free

Author :
Release : 2022-07-25
Genre : Mathematics
Kind : eBook
Book Rating : 028/5 ( reviews)

GET EBOOK


Book Synopsis Human-Like Decision Making and Control for Autonomous Driving by : Peng Hang

Download or read book Human-Like Decision Making and Control for Autonomous Driving written by Peng Hang. This book was released on 2022-07-25. Available in PDF, EPUB and Kindle. Book excerpt: 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 applied to human-like decision making, enabling the autonomous vehicle and the human driver interaction to be modelled using noncooperative game theory approach. It also uses game theory to model collaborative decision making between connected autonomous vehicles. This framework enables human-like decision making and control of autonomous vehicles, which leads to safer and more efficient driving in complicated traffic scenarios. The book will be of interest to students and professionals alike, in the field of automotive engineering, computer engineering and control engineering.

Autonomous Intelligent Vehicles

2011-11-15 Computers
Download Autonomous Intelligent Vehicles PDF Online Free

Author :
Release : 2011-11-15
Genre : Computers
Kind : eBook
Book Rating : 801/5 ( reviews)

GET EBOOK


Book Synopsis Autonomous Intelligent Vehicles by : Hong Cheng

Download or read book Autonomous Intelligent Vehicles written by Hong Cheng. This book was released on 2011-11-15. Available in PDF, EPUB and Kindle. Book excerpt: This important text/reference presents state-of-the-art research on intelligent vehicles, covering not only topics of object/obstacle detection and recognition, but also aspects of vehicle motion control. With an emphasis on both high-level concepts, and practical detail, the text links theory, algorithms, and issues of hardware and software implementation in intelligent vehicle research. Topics and features: presents a thorough introduction to the development and latest progress in intelligent vehicle research, and proposes a basic framework; provides detection and tracking algorithms for structured and unstructured roads, as well as on-road vehicle detection and tracking algorithms using boosted Gabor features; discusses an approach for multiple sensor-based multiple-object tracking, in addition to an integrated DGPS/IMU positioning approach; examines a vehicle navigation approach using global views; introduces algorithms for lateral and longitudinal vehicle motion control.

Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles

2022-06-01 Technology & Engineering
Download Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles PDF Online Free

Author :
Release : 2022-06-01
Genre : Technology & Engineering
Kind : eBook
Book Rating : 068/5 ( reviews)

GET EBOOK


Book Synopsis Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles by : Li Yeuching

Download or read book Deep Reinforcement Learning-based Energy Management for Hybrid Electric Vehicles written by Li Yeuching. This book was released on 2022-06-01. Available in PDF, EPUB and Kindle. Book excerpt: The urgent need for vehicle electrification and improvement in fuel efficiency has gained increasing attention worldwide. Regarding this concern, the solution of hybrid vehicle systems has proven its value from academic research and industry applications, where energy management plays a key role in taking full advantage of hybrid electric vehicles (HEVs). There are many well-established energy management approaches, ranging from rules-based strategies to optimization-based methods, that can provide diverse options to achieve higher fuel economy performance. However, the research scope for energy management is still expanding with the development of intelligent transportation systems and the improvement in onboard sensing and computing resources. Owing to the boom in machine learning, especially deep learning and deep reinforcement learning (DRL), research on learning-based energy management strategies (EMSs) is gradually gaining more momentum. They have shown great promise in not only being capable of dealing with big data, but also in generalizing previously learned rules to new scenarios without complex manually tunning. Focusing on learning-based energy management with DRL as the core, this book begins with an introduction to the background of DRL in HEV energy management. The strengths and limitations of typical DRL-based EMSs are identified according to the types of state space and action space in energy management. Accordingly, value-based, policy gradient-based, and hybrid action space-oriented energy management methods via DRL are discussed, respectively. Finally, a general online integration scheme for DRL-based EMS is described to bridge the gap between strategy learning in the simulator and strategy deployment on the vehicle controller.

You may also like...