Author: Ting Xu
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Connected and Autonomous Vehicles (CAVs) provide the opportunity for signal-free intersection navigation. This thesis introduces and demonstrates a centralized cooperative driving algorithm that considers two vehicles approaching a non-signalized multi-way intersection where the safe traversal can be negotiated. It is assumed that the incoming and outgoing directions are known, and individual vehicle velocities are controllable within a specified range of acceleration and for a specified range from the intersection.The proposed algorithm is developed by first considering the time-space interval of possible intersections between the vehicles. This leads to the development of a set of collision patterns that predict intersection situations that do not need to be negotiated. It is shown that these patterns extend readily from two-way intersections to eight-way intersections. In cases where path conflicts are detected within the intersection, the algorithm seeks to minimize the complexity of multi-vehicle coordination by preventing any speed deviation of the first vehicle passing through the intersection. The proposed solution in the algorithm is to redesign velocity profiles of the second vehicle arriving at the intersection, thereby avoiding any interference in the planned trajectory of the first vehicle.The algorithm is agnostic to the number of directions in/out of the intersection, and is readily generalized for ranges in acceleration limits and interaction ranges between vehicles. Based on the different cases where two vehicles original trajectories can cause potential collisions, simulation results show the effectiveness of the algorithm under different approaches, such as allowable velocity ranges, accelerations, and minimum algorithm starting distances.
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:
Publisher:
ISBN: 9789055843077
Category :
Languages : en
Pages : 157
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Book Description
Author: Lars Braubach
Publisher: Springer Nature
ISBN: 3031291042
Category : Technology & Engineering
Languages : en
Pages : 314
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Book Description
This book includes the latest research in the diverse field of intelligent distributed computing, covering a multitude of aspects in both distributed computing and intelligent systems. It includes contributions in machine learning, distributed systems & agents, text- and research-centric applications, social systems, and smart cities. It was written by leading experts in the field, who presented their work as part of the 15th International Symposium on Intelligent Distributed Computing (IDC 2022).
Author: Evon Abu-Taieh
Publisher:
ISBN: 178985363X
Category :
Languages : en
Pages : 83
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Book Description
Author: Anand Paul
Publisher: Elsevier
ISBN: 0128095466
Category : Transportation
Languages : en
Pages : 244
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Book Description
Intelligent Vehicular Network and Communications: Fundamentals, Architectures and Solutions begins with discussions on how the transportation system has transformed into today’s Intelligent Transportation System (ITS). It explores the design goals, challenges, and frameworks for modeling an ITS network, discussing vehicular network model technologies, mobility management architectures, and routing mechanisms and protocols. It looks at the Internet of Vehicles, the vehicular cloud, and vehicular network security and privacy issues. The book investigates cooperative vehicular systems, a promising solution for addressing current and future traffic safety needs, also exploring cooperative cognitive intelligence, with special attention to spectral efficiency, spectral scarcity, and high mobility. In addition, users will find a thorough examination of experimental work in such areas as Controller Area Network protocol and working function of On Board Unit, as well as working principles of roadside unit and other infrastructural nodes. Finally, the book examines big data in vehicular networks, exploring various business models, application scenarios, and real-time analytics, concluding with a look at autonomous vehicles. Proposes cooperative, cognitive, intelligent vehicular networks Examines how intelligent transportation systems make more efficient transportation in urban environments Outlines next generation vehicular networks technology
Author: Kyandoghere Kyamakya
Publisher: MDPI
ISBN: 3036508481
Category : Technology & Engineering
Languages : en
Pages : 494
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Book Description
Building around innovative services related to different modes of transport and traffic management, intelligent transport systems (ITS) are being widely adopted worldwide to improve the efficiency and safety of the transportation system. They enable users to be better informed and make safer, more coordinated, and smarter decisions on the use of transport networks. Current ITSs are complex systems, made up of several components/sub-systems characterized by time-dependent interactions among themselves. Some examples of these transportation-related complex systems include: road traffic sensors, autonomous/automated cars, smart cities, smart sensors, virtual sensors, traffic control systems, smart roads, logistics systems, smart mobility systems, and many others that are emerging from niche areas. The efficient operation of these complex systems requires: i) efficient solutions to the issues of sensors/actuators used to capture and control the physical parameters of these systems, as well as the quality of data collected from these systems; ii) tackling complexities using simulations and analytical modelling techniques; and iii) applying optimization techniques to improve the performance of these systems.
Author: Management Association, Information Resources
Publisher: IGI Global
ISBN: 1466684747
Category : Technology & Engineering
Languages : en
Pages : 1735
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Book Description
From driverless cars to vehicular networks, recent technological advances are being employed to increase road safety and improve driver satisfaction. As with any newly developed technology, researchers must take care to address all concerns, limitations, and dangers before widespread public adoption. Transportation Systems and Engineering: Concepts, Methodologies, Tools, and Applications addresses current trends in transportation technologies, such as smart cars, green technologies, and infrastructure development. This multivolume book is a critical reference source for engineers, computer scientists, transportation authorities, students, and practitioners in the field of transportation systems management.
Author: Fei Hu
Publisher: CRC Press
ISBN: 100087723X
Category : Computers
Languages : en
Pages : 357
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Book Description
Deep Learning (DL) is an effective approach for AI-based vehicular networks and can deliver a powerful set of tools for such vehicular network dynamics. In various domains of vehicular networks, DL can be used for learning-based channel estimation, traffic flow prediction, vehicle trajectory prediction, location-prediction-based scheduling and routing, intelligent network congestion control mechanism, smart load balancing and vertical handoff control, intelligent network security strategies, virtual smart and efficient resource allocation and intelligent distributed resource allocation methods. This book is based on the work from world-famous experts on the application of DL for vehicle networks. It consists of the following five parts: (I) DL for vehicle safety and security: This part covers the use of DL algorithms for vehicle safety or security. (II) DL for effective vehicle communications: Vehicle networks consist of vehicle-to-vehicle and vehicle-to-roadside communications. This part covers how Intelligent vehicle networks require a flexible selection of the best path across all vehicles, adaptive sending rate control based on bandwidth availability and timely data downloads from a roadside base-station. (III) DL for vehicle control: The myriad operations that require intelligent control for each individual vehicle are discussed in this part. This also includes emission control, which is based on the road traffic situation, the charging pile load is predicted through DL andvehicle speed adjustments based on the camera-captured image analysis. (IV) DL for information management: This part covers some intelligent information collection and understanding. We can use DL for energy-saving vehicle trajectory control based on the road traffic situation and given destination information; we can also natural language processing based on DL algorithm for automatic internet of things (IoT) search during driving. (V) Other applications. This part introduces the use of DL models for other vehicle controls. Autonomous vehicles are becoming more and more popular in society. The DL and its variants will play greater roles in cognitive vehicle communications and control. Other machine learning models such as deep reinforcement learning will also facilitate intelligent vehicle behavior understanding and adjustment. This book will become a valuable reference to your understanding of this critical field.
Author: James M. Anderson
Publisher: Rand Corporation
ISBN: 0833084372
Category : Transportation
Languages : en
Pages : 215
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Book Description
The automotive industry appears close to substantial change engendered by “self-driving” technologies. This technology offers the possibility of significant benefits to social welfare—saving lives; reducing crashes, congestion, fuel consumption, and pollution; increasing mobility for the disabled; and ultimately improving land use. This report is intended as a guide for state and federal policymakers on the many issues that this technology raises.
