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Author: Peng Hang Publisher: CRC Press ISBN: 1000624951 Category : Mathematics Languages : en Pages : 201
Book Description
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.
Author: Peng Hang Publisher: CRC Press ISBN: 1000624951 Category : Mathematics Languages : en Pages : 201
Book Description
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.
Author: Peng Hang Publisher: CRC Press ISBN: 1000625028 Category : Mathematics Languages : en Pages : 237
Book Description
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.
Author: Jorge Villagra Publisher: Elsevier ISBN: 0323985491 Category : Technology & Engineering Languages : en Pages : 426
Book Description
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
Author: Dimia Iberraken Publisher: ISBN: Category : Languages : en Pages : 0
Book Description
Recent advances in Autonomous Vehicles (AV) driving raised up all the importance to ensure the complete reliability of AV maneuvers even in highly dynamic and uncertain environments/situations. This objective becomes even more challenging due to the uniqueness of every traffic situation/condition. To cope with all these very constrained and complex configurations, AVs must have appropriate control architecture with reliable and real-time Risk Assessment and Management Strategies (RAMS). These targeted RAMS must lead to reduce drastically the navigation risks (theoretically, lower than any human-like driving behavior), with a systemic way. Consequently, the aim is also to reduce the need for too extensive testing (which could take several months and years for each produced RAMS without at the end having absolute prove). Hence the goal in this Ph.D. thesis is to have a provable methodology for AV RAMS. This dissertation addresses the full pipeline from risk assessment, path planning to decision-making and control of autonomous vehicles. In the first place, an overall Probabilistic Multi-Controller Architecture (P-MCA) is designed for safe autonomous driving under uncertainties. The P-MCA is composed of several interconnected modules that are responsible for: assessing the collision risk with all observed vehicles while considering their trajectories' predictions; planning the different driving maneuvers; making the decision on the most suitable actions to achieve; control the vehicle movement; aborting safely the engaged maneuver if necessary (due for instance to a sudden change in the environment); and as last resort planning evasive actions if there is no other choice. The proposed risk assessment is based on a dual-safety stage strategy. The first stage analyzes the actual driving situation and predicts potential collisions. This is performed while taking into consideration several dynamic constraints and traffic conditions that are known at the time of planning. The second stage is applied in real-time, during the maneuver achievement, where a safety verification mechanism is activated to quantify the risks and the criticality of the driving situation beyond the remaining time to achieve the maneuver. The decision-making strategy is based on a Sequential Decision Networks for Maneuver Selection and Verification (SDN-MSV) and corresponds to an important module of the P-MCA. This module is designed to manage several road maneuvers under uncertainties. It utilizes the defined safety stages assessment to propose discrete actions that allow to: derive appropriate maneuvers in a given traffic situation and provide a safety retrospection that updates in real-time the ego-vehicle movements according to the environment dynamic, in order to face any sudden hazardous and risky situation. In the latter case, it is proposed to compute the corresponding low-level control based on the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) that allows the ego-vehicle to pursue the advised collision-free evasive trajectory to avert an accident and to guarantee safety at any time.The reliability and the flexibility of the overall proposed P-MCA and its elementary components have been intensively validated, first in simulated traffic conditions, with various driving scenarios, and secondly, in real-time with the autonomous vehicles available at Institut Pascal.
Author: Chen Lv Publisher: SAE International ISBN: 1468604325 Category : Technology & Engineering Languages : en Pages : 28
Book Description
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
Author: Kasra Mokhtari Publisher: ISBN: Category : Languages : en Pages :
Book Description
How can autonomous vehicles offer safer behavior by accounting for social information? Social information includes not only information about the number of pedestrians, but also pedestrians' behavior, age, course of action, etc. While driving, the interaction of a vehicle and the other road users is complicated because each operator acts dynamically and according to their own will, thus creating additional uncertainties for an autonomous vehicle to consider. To address some of these uncertainties and to avoid collisions human drivers use a variety of tricks and heuristics learned during their time driving. However, substituting human drivers with autonomous control systems comes at the price of eliminating the underlying social intelligence of human drivers that makes these predictions possible. Steps should, therefore, be taken to imbue autonomous vehicles with the ability to use social information to increase safety since information about the social environment may provide autonomous vehicles with valuable data influencing how these systems select and moderate their actions. This dissertation develops well-defined methods that will enable an autonomous vehicle to use social information to adjust the vehicle's course of action with the hope of providing a much safer environment for pedestrians, other car drivers, and AV passengers. We first generate our social information dataset by repeatedly driving in State College, PA along the different paths. We then present an initial examination of how social information (i.e. pedestrian density) could be used first for path recognition and then for predicting the number of pedestrians that the vehicle will encounter in the future which is intuitively related to the risk of traveling down a path for autonomous vehicles. Moreover, we develop a method for an AV operating near a college campus to evaluate the risk associated with different options and to select the minimal risk option in the hope of improving safety. We then design a decision-making framework for controlling an autonomous vehicle as it navigates through an unsignalized intersection crowded with pedestrians in both cases where it receives true state of the environment and noisy observations. We hope that the research presented in this dissertation will inspire future researchers to develop autonomous vehicles that more intelligently and efficiently account for pedestrian information in their decision-making framework to make a collision-free world.
Author: Antonio Artuñedo Publisher: Springer Nature ISBN: 3030459055 Category : Technology & Engineering Languages : en Pages : 205
Book Description
This book describes an effective decision-making and planning architecture for enhancing the navigation capabilities of automated vehicles in the presence of non-detailed, open-source maps. The system involves dynamically obtaining road corridors from map information and utilizing a camera-based lane detection system to update and enhance the navigable space in order to address the issues of intrinsic uncertainty and low-fidelity. An efficient and human-like local planner then determines, within a probabilistic framework, a safe motion trajectory, ensuring the continuity of the path curvature and limiting longitudinal and lateral accelerations. LiDAR-based perception is then used to identify the driving scenario, and subsequently re-plan the trajectory, leading in some cases to adjustment of the high-level route to reach the given destination. The method has been validated through extensive theoretical and experimental analyses, which are reported here in detail.
Author: Peng Hang
Author: Peng Hang
Author: Dapeng Liu
Author: Peng Hang
Author: Jorge Villagra
Author: Sam Hind
Author: Dimia Iberraken
Author: Chen Lv
Author: Wei Huang
Author: Kasra Mokhtari
Author: Antonio Artuñedo