Decision Making Planning And Control Strategies For Intelligent Vehicles
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Decision Making Planning And Control Strategies For Intelligent Vehicles
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Author : Haotian Cao
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Decision Making Planning And Control Strategies For Intelligent Vehicles written by Haotian Cao and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Technology & Engineering categories.
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 Planning And Control Strategies For Intelligent Vehicles
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Author : Haotian Cao
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2020-07-28
Decision Making Planning And Control Strategies For Intelligent Vehicles written by Haotian Cao and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-07-28 with Computers categories.
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
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Author : Jorge Villagra
language : en
Publisher: Elsevier
Release Date : 2023-03-03
Decision Making Techniques For Autonomous Vehicles written by Jorge Villagra and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-03 with Technology & Engineering categories.
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 Automated Driving
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Author : Chen Lv
language : en
Publisher: SAE International
Release Date : 2022-03-11
Human Like Decision Making And Control For Automated Driving written by Chen Lv and has been published by SAE International this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-03-11 with Technology & Engineering categories.
The on-vehicle automation system is primarily designed to replace the human driver during driving to enhance the performance and avoid possible fatalities. However, current implementations in automated vehicles (AVs) generally neglect that human imperfection and preference do not always lead to negative consequences, which prevents achieving optimized vehicle performance and maximized road safety. Human-like Decision-making and Control for Automated Driving takes a step forward to address breaking through the limitation of future automation applications, investigating in depth: Human driving feature modeling and analysis Personalized motion control for AVs Human-like decision making for AVs Click here to access the full SAE EDGETM Research Report portfolio. https://doi.org/10.4271/EPR2022005
Deep Reinforcement Learning Based Energy Management For Hybrid Electric Vehicles
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Author : Yeuching Li
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2022-02-14
Deep Reinforcement Learning Based Energy Management For Hybrid Electric Vehicles written by Yeuching Li and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-14 with Computers categories.
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.
Behavior Analysis And Modeling Of Traffic Participants
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Author : Xiaolin Song
language : en
Publisher: Springer Nature
Release Date : 2022-06-01
Behavior Analysis And Modeling Of Traffic Participants written by Xiaolin Song and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-01 with Technology & Engineering categories.
A road traffic participant is a person who directly participates in road traffic, such as vehicle drivers, passengers, pedestrians, or cyclists, however, traffic accidents cause numerous property losses, bodily injuries, and even deaths to them. To bring down the rate of traffic fatalities, the development of the intelligent vehicle is a much-valued technology nowadays. It is of great significance to the decision making and planning of a vehicle if the pedestrians' intentions and future trajectories, as well as those of surrounding vehicles, could be predicted, all in an effort to increase driving safety. Based on the image sequence collected by onboard monocular cameras, we use the Long Short-Term Memory (LSTM) based network with an enhanced attention mechanism to realize the intention and trajectory prediction of pedestrians and surrounding vehicles. However, although the fully automatic driving era still seems far away, human drivers are still a crucial part of the road‒driver‒vehicle system under current circumstances, even dealing with low levels of automatic driving vehicles. Considering that more than 90 percent of fatal traffic accidents were caused by human errors, thus it is meaningful to recognize the secondary task while driving, as well as the driving style recognition, to develop a more personalized advanced driver assistance system (ADAS) or intelligent vehicle. We use the graph convolutional networks for spatial feature reasoning and the LSTM networks with the attention mechanism for temporal motion feature learning within the image sequence to realize the driving secondary-task recognition. Moreover, aggressive drivers are more likely to be involved in traffic accidents, and the driving risk level of drivers could be affected by many potential factors, such as demographics and personality traits. Thus, we will focus on the driving style classification for the longitudinal car-following scenario. Also, based on the Structural Equation Model (SEM) and Strategic Highway Research Program 2 (SHRP 2) naturalistic driving database, the relationships among drivers' demographic characteristics, sensation seeking, risk perception, and risky driving behaviors are fully discussed. Results and conclusions from this short book are expected to offer potential guidance and benefits for promoting the development of intelligent vehicle technology and driving safety.
Handbook Of Power Electronics In Autonomous And Electric Vehicles
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Author : Muhammad H. Rashid
language : en
Publisher: Elsevier
Release Date : 2024-07-22
Handbook Of Power Electronics In Autonomous And Electric Vehicles written by Muhammad H. Rashid and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-22 with Technology & Engineering categories.
Handbook of Power Electronics in Autonomous and Electric Vehicles provides advanced knowledge on autonomous systems, electric propulsion in electric vehicles, radars and sensors for autonomous systems, and relevant aspects of energy storage and battery charging. The work is designed to provide clear technical presentation with a focus on commercial viability. It supports any and all aspects of a project requiring specialist design, analysis, installation, commissioning and maintenance services. With this book in hand, engineers will be able to execute design, analysis and evaluation of assigned projects using sound engineering principles and commercial requirements, policies, and product and program requirements. - Presents core power systems and engineering applications relevant to autonomous and electric vehicles in characteristic depth and technical presentation - Offers practical support and guidance with detailed examples and applications for laboratory vehicular test plans and automotive field experimentation - Includes modern technical coverage of emergent fields, including sensors and radars, battery charging and monitoring, and vehicle cybersecurity
Control Strategies For Advanced Driver Assistance Systems And Autonomous Driving Functions
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Author : Harald Waschl
language : en
Publisher: Springer
Release Date : 2018-06-28
Control Strategies For Advanced Driver Assistance Systems And Autonomous Driving Functions written by Harald Waschl and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-06-28 with Technology & Engineering categories.
This book describes different methods that are relevant to the development and testing of control algorithms for advanced driver assistance systems (ADAS) and automated driving functions (ADF). These control algorithms need to respond safely, reliably and optimally in varying operating conditions. Also, vehicles have to comply with safety and emission legislation. The text describes how such control algorithms can be developed, tested and verified for use in real-world driving situations. Owing to the complex interaction of vehicles with the environment and different traffic participants, an almost infinite number of possible scenarios and situations that need to be considered may exist. The book explains new methods to address this complexity, with reference to human interaction modelling, various theoretical approaches to the definition of real-world scenarios, and with practically-oriented examples and contributions, to ensure efficient development and testing of ADAS and ADF. Control Strategies for Advanced Driver Assistance Systems and Autonomous Driving Functions is a collection of articles by international experts in the field representing theoretical and application-based points of view. As such, the methods and examples demonstrated in the book will be a valuable source of information for academic and industrial researchers, as well as for automotive companies and suppliers.
Autonomous Intelligent Vehicles
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Author : Dr. N. Rama Devi, Dr. V. Rathinam, Kiran Raj Goli, Dr. T. Hussain
language : en
Publisher: RK Publication
Release Date : 2025-03-07
Autonomous Intelligent Vehicles written by Dr. N. Rama Devi, Dr. V. Rathinam, Kiran Raj Goli, Dr. T. Hussain and has been published by RK Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-07 with Computers categories.
Autonomous Intelligent Vehicles explores the technologies, algorithms, and systems enabling self-driving cars. Covering AI, sensors, control systems, and safety protocols, it provides insights into vehicle autonomy, perception, decision-making, and real-world applications. Ideal for engineers, researchers, and students interested in the future of intelligent transportation and autonomous mobility solutions.
Proceedings Of 4th 2024 International Conference On Autonomous Unmanned Systems 4th Icaus 2024
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Author : Lianqing Liu
language : en
Publisher: Springer Nature
Release Date : 2025-05-02
Proceedings Of 4th 2024 International Conference On Autonomous Unmanned Systems 4th Icaus 2024 written by Lianqing Liu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-02 with Technology & Engineering categories.
This book includes original, peer-reviewed research papers from the 4th ICAUS 2024, which provides a unique and engaging platform for scientists, engineers and practitioners from all over the world to present and share their most recent research results and innovative ideas. The 4th ICAUS 2024 aims to stimulate researchers working in areas relevant to intelligent unmanned systems. Topics covered include but are not limited to: Unmanned Aerial/Ground/Surface/Underwater Systems, Robotic, Autonomous Control/Navigation and Positioning/ Architecture, Energy and Task Planning and Effectiveness Evaluation Technologies, Artificial Intelligence Algorithm/Bionic Technology and their Application in Unmanned Systems. The papers presented here share the latest findings in unmanned systems, robotics, automation, intelligent systems, control systems, integrated networks, modelling and simulation. This makes the book a valuable resource for researchers, engineers and students alike.