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Author: Abdullah Baz
Publisher:
ISBN:
Category : Autonomous vehicles
Languages : en
Pages : 98
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Book Description
One of the most critical subjects in Intelligent Transportation System (ITS) nowadays is the autonomous vehicle (AV). It is rapidly improving, and it will have a substantial positive effect on traffic safety and efficiency. Most of auto manufacturer companies and tech industries are spending a lot of money on research for developing autonomous vehicles. AV would have an excellent contribution to managing and controlling intersections. This study introduces a decision-making algorithm for autonomous vehicles at an intersection to optimize the intersection capacity and minimize delay time by using Game Theory mathematical models. This model using vehicle-to-infrastructure (V2I) communication features that will be available in AV so that vehicles are able to communicate with roadside unit (RSU) and with each other to determine which one goes first, depending on different factors such as their speeds and locations, and vehicle size, taking in consideration the safety of the vehicles so we can have collision free intersection. Two different mathematical models were developed; one with %100 autonomous vehicles and the other one is when we have mix traffic, autonomous vehicles, and ordinary vehicles. A simulation model was developed using a standard microscopic simulation platform VISSIM to implement this algorithm. A comparison of the proposed method and two other ordinary intersection control method; traffic lights, and roundabout was made to calculate the total delay of the intersection for each intersection management method. The simulation ran on three different traffic volume, High, moderate, and low volume. Moreover, three different speeds for each traffic volume. The results shows that the proposed system reduces the total delay by more than 65 percent compared with the roundabout, and about 85 percent comparing with a signalized intersection. Another simulation was done for the second scenario, mixed traffic, also a comparison between the proposed methods; roundabout, and the signalized intersection was made for the same cases of various speeds and volume. For model two, results show 30% reduction in delay compared to the roundabout and 89% compared to signalized intersections.
Author: Abdullah Baz
Publisher:
ISBN:
Category : Autonomous vehicles
Languages : en
Pages : 98
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Book Description
One of the most critical subjects in Intelligent Transportation System (ITS) nowadays is the autonomous vehicle (AV). It is rapidly improving, and it will have a substantial positive effect on traffic safety and efficiency. Most of auto manufacturer companies and tech industries are spending a lot of money on research for developing autonomous vehicles. AV would have an excellent contribution to managing and controlling intersections. This study introduces a decision-making algorithm for autonomous vehicles at an intersection to optimize the intersection capacity and minimize delay time by using Game Theory mathematical models. This model using vehicle-to-infrastructure (V2I) communication features that will be available in AV so that vehicles are able to communicate with roadside unit (RSU) and with each other to determine which one goes first, depending on different factors such as their speeds and locations, and vehicle size, taking in consideration the safety of the vehicles so we can have collision free intersection. Two different mathematical models were developed; one with %100 autonomous vehicles and the other one is when we have mix traffic, autonomous vehicles, and ordinary vehicles. A simulation model was developed using a standard microscopic simulation platform VISSIM to implement this algorithm. A comparison of the proposed method and two other ordinary intersection control method; traffic lights, and roundabout was made to calculate the total delay of the intersection for each intersection management method. The simulation ran on three different traffic volume, High, moderate, and low volume. Moreover, three different speeds for each traffic volume. The results shows that the proposed system reduces the total delay by more than 65 percent compared with the roundabout, and about 85 percent comparing with a signalized intersection. Another simulation was done for the second scenario, mixed traffic, also a comparison between the proposed methods; roundabout, and the signalized intersection was made for the same cases of various speeds and volume. For model two, results show 30% reduction in delay compared to the roundabout and 89% compared to signalized intersections.
Author: Peng Hang
Publisher: CRC Press
ISBN: 1000624951
Category : Mathematics
Languages : en
Pages : 201
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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: Mahmoud Faraj
Publisher:
ISBN:
Category : Autonomous vehicles
Languages : en
Pages : 110
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Book Description
Road congestion in urban environments, especially near signalized intersections, has been a major cause of significant fuel and time waste. Various solutions have been proposed to solve the problem of increasing idling times and number of stops of vehicles at signalized intersections, ranging from infrastructure-based techniques, such as dynamic traffic light control systems, to vehicle-based techniques that rely on optimal speed computation. However, all of the vehicle-based solutions introduced to solve the problem have approached the problem from a single vehicle point of view. Speed optimization for vehicles approaching a traffic light is an individual decision-making process governed by the actions/decisions of the other vehicles sharing the same traffic light. Since the optimization of other vehicles' speed decisions is not taken into consideration, vehicles selfishly compete over the available green light; as a result, some of them experience unnecessary delay which may lead to increasing congestion. In addition, the integration of dynamic traffic light control system with vehicle speed optimization such that coordination and cooperation between the traffic light and vehicles themselves has not yet been addressed. As a step toward technological solutions to popularize the use of autonomous vehicles, this thesis introduces a game theoretic-based cooperative speed optimization framework to minimize the idling times and number of stops of vehicles at signalized intersections. This framework consists of three modules to cover issues of autonomous vehicle individual speed optimization, information acquisition and conflict recognition, and cooperative speed decision making. It relies on a linear programming optimization technique and game theory to allow autonomous vehicles heading toward a traffic light cooperate and agree on certain speed actions such that the average idling times and number of stops are minimized. In addition, the concept of bargaining in game theory is introduced to allow autonomous vehicles trade their right of passing the traffic light with less or without any stops. Furthermore, a dynamic traffic light control system is introduced to allow the cooperative autonomous vehicles cooperate and coordinate with the traffic light to further minimize their idling times and number of stops. Simulation has been conducted in MATLAB to test and validate the proposed framework under various traffic conditions and results are reported showing significant reductions of average idling times and number of stops for vehicles using the proposed framework as compared to a non-cooperative speed optimization algorithm. Moreover, a platoon-based autonomous vehicle speed optimization scheme is posed to minimize the average idling times and number of stops for autonomous vehicles connected in platoons. This platoon-based scheme consists of a linear programming optimization technique and intelligent vehicle decision-making algorithm to allow vehicles connected in a platoon and approaching a signalized intersection decide in a decentralized manner whether it is efficient to be part of the platoon or not. Simulation has been conducted in MATLAB to investigate the performance of this platoon-based scheme under various traffic conditions and results are reported, showing that vehicles using the proposed scheme achieve lower average values of idling times and number of stops as compared to two other platoon scenarios.
Author: Sezgin Ersoy
Publisher:
ISBN:
Category : Electronic books
Languages : en
Pages : 0
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Book Description
Self-driven vehicles slowly but surely are making the transition from a distant future technology to current luxury by slowly becoming a part of our everyday life. Due to their self-driving ability, they are making our travels efficient. However, they are still a work in progress as they require many software and hardware-based improvements. To address the software part of this issue, an image processing-based solution has been proposed in this study. The algorithm estimates the real-time positions and predicts the possible interaction of the objects, such as other moving vehicles, present in the vicinity of the driven autonomous vehicle in determined environmental conditions. Cameras and related peripheral present on autonomous vehicles are used to obtain data related to the real-time situation for predicting and preventing possible hazardous events, such as accidents, using these data.
Author: Peng Hang
Publisher: CRC Press
ISBN: 1000625028
Category : Mathematics
Languages : en
Pages : 237
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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: Haotian Cao
Publisher: Springer Nature
ISBN: 3031015061
Category : Technology & Engineering
Languages : en
Pages : 128
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Book Description
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.
Author: Sam Hind
Publisher: Springer Nature
ISBN: 9819717493
Category :
Languages : en
Pages : 278
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Book Description
Author: Kasra Mokhtari
Publisher:
ISBN:
Category :
Languages : en
Pages :
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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: Jorge Villagra
Publisher: Elsevier
ISBN: 0323985491
Category : Technology & Engineering
Languages : en
Pages : 426
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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: William Frere Lawless
Publisher: Frontiers Media SA
ISBN: 283251930X
Category : Science
Languages : en
Pages : 220
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Book Description
