A Scenario-Reconfigurable Simulator for Verifying Service-Oriented Cooperation Mechanisms and Policies of Connected Intelligent Vehicles

2019 ◽  
Vol 7 (1) ◽  
pp. 44-62
Author(s):  
Kailong Zhang ◽  
Xiaowu Li ◽  
Ce Xie ◽  
Yujia Wang ◽  
Liuyang Li ◽  
...  
Keyword(s):  
Communication Technology ◽  
Intelligent Transportation Systems ◽  
Large Scale ◽  
Smart Cities ◽  
Transportation Systems ◽  
Intelligent Vehicles ◽  
Simulation Functions ◽  
Service Oriented ◽  
Series Of Experiments ◽  
Setting Parameters

With the emerging vehicular network and the possible diverse applications, intelligent transportation systems (ITS) have been evolving to Cooperative ITS (C-ITS) with connected intelligent vehicles, and the topics in this field have raised more and more research interests recently. However, subjecting to the immaturity of V2X communication technology, the difficulty and high cost to deploy such large scale ITS with intelligent vehicles, emerging studies are stuck with the verification of these big C-ITS. As more and more expected, intelligent vehicles will play important roles in the future smart cities and societies, as diverse mobility carriers. Focusing on new features of these carriers, mainly covering cyber-physical fusion, vehicular networking, service-carrier and so on, one new ITS simulator QoS-CITS for such service-oriented C-ITS is designed and developed. To enhance the adaptability, a scenario reconfigurable architecture is firstly designed, in which scenes can be described via XML file. On this basis, the authors have implemented all reservation-based models of traffic objects, state-driven behaviors, cooperation mechanisms, and policies, which are proposed for service-oriented C-ITS. Through a series of experiments are conducted with different parameters and typical scenes, all simulation functions are efficiently verified. And finally, some important conclusions drawn from large amount of experiments via QoS-CITS are exhibited. It's important to note that, researchers can conduct various experiments, both the traditional Passing-Through-Intersection (PTI) problem and service-oriented cooperation, via setting parameters of QoS-CITS according to their requirements, and can also analyze the performance with statistics data recorded automatically.

scholarly journals Service-Oriented Cooperation Policies for Intelligent Ground Vehicles Approaching Intersections

2018 ◽  
Vol 8 (9) ◽  
pp. 1647 ◽  
Author(s):  
Kailong Zhang ◽  
Ce Xie ◽  
Yujia Wang ◽  
Min Wang ◽  
Arnaud Fortelle ◽  
...  
Keyword(s):  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Transportation Systems ◽  
Intelligent Vehicles ◽  
Vehicular Communication ◽  
Ground Vehicles ◽  
Traffic Efficiency ◽  
Single Vehicle ◽  
Service Oriented ◽  
Major Factors

With the coming of intelligent vehicles and vehicular communication, Intelligent Transportation Systems (ITS) of connected vehicles are emerging and now evolving to Cooperative-ITS (C-ITS), as service platforms for smart cities. Considering new service properties, the autonomous cooperation of such vehicles has exhibited novel QoS features that imply new requirements: guaranteeing the traffic efficiency of any emergent vehicle while trying to promote the throughput at an intersection. So, after analyzing the classic reservation-based cooperation mechanisms, new QoS-oriented cooperation methods and policies are studied in this work. Concretely, several models of related traffic objects we have proposed are firstly introduced briefly. Then, the scheduling policies of vehicles approaching an intersection have been presented, including three existing policies (FAFP-SV, FAFP-SQ, and HQEP-SV) and five new polices (FAFP-SQ-SV, FAFP-MQ, HWFP-SQ, HWFP-SQ-SV, HWFP-MQ). These policies combine two major factors: vehicular priority for scheduling and concurrency in traffics. The first one includes the vehicular arrival-time, priority mapped to QoS, and the weight of reserved vehicles on a lane etc. In addition, the second refers to schedule a platoon rather than single vehicle each time, or platoons on different lanes instead of one platoon on only one lane. All these policies have been implemented, and further, verified within the parameter-configurable traffic simulator QoS-CITS (v2.1) we designed and developed with C#. Abundant experiments have been conducted with configured typical traffic scenes, and experimental results show that HWFP-SQ-SV and HWFP-MQ can guarantee both the QoS of emergent vehicles and traffic throughput better than other six policies.

scholarly journals Optimization and Evaluation of Platooning Car-Following Models in a Connected Vehicle Environment

2021 ◽  
Vol 13 (6) ◽  
pp. 3474
Author(s):  
Guang Yu ◽  
Shuo Liu ◽  
Qiangqiang Shangguan
Keyword(s):  
Communication Technology ◽  
Intelligent Transportation Systems ◽  
Rapid Development ◽  
Safety Margin ◽  
Practical Method ◽  
Transportation Systems ◽  
Systematic Evaluation ◽  
Connected Vehicle ◽  
Car Following ◽  
Cooperative Driving

With the rapid development of information and communication technology, future intelligent transportation systems will exhibit a trend of cooperative driving of connected vehicles. Platooning is an important application technique for cooperative driving. Herein, optimized car-following models for platoon control based on intervehicle communication technology are proposed. On the basis of existing indicators, a series of evaluation methods for platoon safety, stability, and energy consumption is constructed. Numerical simulations are used to compare the effects of three traditional models and their optimized counterparts on the car-following process. Moreover, the influence of homogenous and heterogeneous attributes on the platoon is analyzed. The optimized model proposed in this paper can improve the stability and safety of vehicle following and reduce the total fuel consumption. The simulation results show that a homogenous platoon can enhance the overall stability of the platoon and that the desired safety margin (DSM) model is better suited for heterogeneous platoon control than the other two models. This paper provides a practical method for the design and systematic evaluation of a platoon control strategy, which is one of the key focuses in the connected and autonomous vehicle industry.

scholarly journals GNSS RUMS: GNSS Realistic Urban Multiagent Simulator for Collaborative Positioning Research

2021 ◽  
Vol 13 (4) ◽  
pp. 544
Author(s):  
Guohao Zhang ◽  
Bing Xu ◽  
Hoi-Fung Ng ◽  
Li-Ta Hsu
Keyword(s):  
Urban Area ◽  
Intelligent Transportation Systems ◽  
Urban Areas ◽  
Large Scale ◽  
Transportation Systems ◽  
Accurate Localization ◽  
Gnss Receivers ◽  
Multipath Signal ◽  
Scale Test ◽  
Error Behavior

Accurate localization of road agents (GNSS receivers) is the basis of intelligent transportation systems, which is still difficult to achieve for GNSS positioning in urban areas due to the signal interferences from buildings. Various collaborative positioning techniques were recently developed to improve the positioning performance by the aid from neighboring agents. However, it is still challenging to study their performances comprehensively. The GNSS measurement error behavior is complicated in urban areas and unable to be represented by naive models. On the other hand, real experiments requiring numbers of devices are difficult to conduct, especially for a large-scale test. Therefore, a GNSS realistic urban measurement simulator is developed to provide measurements for collaborative positioning studies. The proposed simulator employs a ray-tracing technique searching for all possible interferences in the urban area. Then, it categorizes them into direct, reflected, diffracted, and multipath signal to simulate the pseudorange, C/N0, and Doppler shift measurements correspondingly. The performance of the proposed simulator is validated through real experimental comparisons with different scenarios based on commercial-grade receivers. The proposed simulator is also applied with different positioning algorithms, which verifies it is sophisticated enough for the collaborative positioning studies in the urban area.

scholarly journals An Advanced Deep Learning Approach for Multi-Object Counting in Urban Vehicular Environments

2021 ◽  
Vol 13 (12) ◽  
pp. 306
Author(s):  
Ahmed Dirir ◽  
Henry Ignatious ◽  
Hesham Elsayed ◽  
Manzoor Khan ◽  
Mohammed Adib ◽  
...  
Keyword(s):  
Deep Learning ◽  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Weather Conditions ◽  
Research Area ◽  
Transportation Systems ◽  
Detection Accuracy ◽  
Adverse Weather ◽  
Object Counting ◽  
The Impact

Object counting is an active research area that gained more attention in the past few years. In smart cities, vehicle counting plays a crucial role in urban planning and management of the Intelligent Transportation Systems (ITS). Several approaches have been proposed in the literature to address this problem. However, the resulting detection accuracy is still not adequate. This paper proposes an efficient approach that uses deep learning concepts and correlation filters for multi-object counting and tracking. The performance of the proposed system is evaluated using a dataset consisting of 16 videos with different features to examine the impact of object density, image quality, angle of view, and speed of motion towards system accuracy. Performance evaluation exhibits promising results in normal traffic scenarios and adverse weather conditions. Moreover, the proposed approach outperforms the performance of two recent approaches from the literature.

scholarly journals Eco-Efficient Mobility in Smart City Scenarios

2020 ◽  
Vol 12 (20) ◽  
pp. 8443
Author(s):  
Ramon Sanchez-Iborra ◽  
Luis Bernal-Escobedo ◽  
José Santa
Keyword(s):  
Intelligent Transportation Systems ◽  
Smart City ◽  
Smart Cities ◽  
Low Cost ◽  
Transportation Systems ◽  
Area Network ◽  
Wide Area Network ◽  
Fully Integrated ◽  
Personal Mobility ◽  
Traffic Efficiency

Cooperative-Intelligent Transportation Systems (C-ITS) have brought a technological revolution, especially for ground vehicles, in terms of road safety, traffic efficiency, as well as in the experience of drivers and passengers. So far, these advances have been focused on traditional transportation means, leaving aside the new generation of personal vehicles that are nowadays flooding our streets. Together with bicycles and motorcycles, personal mobility devices such as segways or electric scooters are firm sustainable alternatives that represent the future to achieve eco-friendly personal mobility in urban settings. In a near future, smart cities will become hyper-connected spaces where these vehicles should be integrated within the underlying C-ITS ecosystem. In this paper, we provide a wide overview of the opportunities and challenges related to this necessary integration as well as the communication solutions that are already in the market to provide these moving devices with low-cost and efficient connectivity. We also present an On-Board Unit (OBU) prototype with different communication options based on the Low Power Wide Area Network (LPWAN) paradigm and several sensors to gather environmental information to facilitate eco-efficiency services. As the attained results suggest, this module allows personal vehicles to be fully integrated in smart city environments, presenting the possibilities of LoRaWAN and Narrow Band-Internet of Things (NB-IoT) communication technologies to provide vehicle connectivity and enable mobile urban sensing.

scholarly journals Deep Autoencoder Neural Networks for Short-Term Traffic Congestion Prediction of Transportation Networks

Sensors ◽  
2019 ◽  
Vol 19 (10) ◽  
pp. 2229 ◽  
Author(s):  
Sen Zhang ◽  
Yong Yao ◽  
Jie Hu ◽  
Yong Zhao ◽  
Shaobo Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Intelligent Transportation Systems ◽  
Traffic Congestion ◽  
Large Scale ◽  
Transportation Networks ◽  
Transportation Network ◽  
Transportation Systems ◽  
Neural Network Models ◽  
Computation Efficiency ◽  
Congestion Prediction

Traffic congestion prediction is critical for implementing intelligent transportation systems for improving the efficiency and capacity of transportation networks. However, despite its importance, traffic congestion prediction is severely less investigated compared to traffic flow prediction, which is partially due to the severe lack of large-scale high-quality traffic congestion data and advanced algorithms. This paper proposes an accessible and general workflow to acquire large-scale traffic congestion data and to create traffic congestion datasets based on image analysis. With this workflow we create a dataset named Seattle Area Traffic Congestion Status (SATCS) based on traffic congestion map snapshots from a publicly available online traffic service provider Washington State Department of Transportation. We then propose a deep autoencoder-based neural network model with symmetrical layers for the encoder and the decoder to learn temporal correlations of a transportation network and predicting traffic congestion. Our experimental results on the SATCS dataset show that the proposed DCPN model can efficiently and effectively learn temporal relationships of congestion levels of the transportation network for traffic congestion forecasting. Our method outperforms two other state-of-the-art neural network models in prediction performance, generalization capability, and computation efficiency.

scholarly journals Blockchain for the Internet of Vehicles: A Decentralized IoT Solution for Vehicles Communication Using Ethereum

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 3928 ◽  
Author(s):  
Rateb Jabbar ◽  
Mohamed Kharbeche ◽  
Khalifa Al-Khalifa ◽  
Moez Krichen ◽  
Kamel Barkaoui
Keyword(s):  
Intelligent Transportation Systems ◽  
Secure Communication ◽  
Data Exchange ◽  
Smart Cities ◽  
Transportation Systems ◽  
Smart Devices ◽  
The Internet ◽  
Internet Of Vehicles ◽  
Computing Platform ◽  
Blockchain Technology

The concept of smart cities has become prominent in modern metropolises due to the emergence of embedded and connected smart devices, systems, and technologies. They have enabled the connection of every “thing” to the Internet. Therefore, in the upcoming era of the Internet of Things, the Internet of Vehicles (IoV) will play a crucial role in newly developed smart cities. The IoV has the potential to solve various traffic and road safety problems effectively in order to prevent fatal crashes. However, a particular challenge in the IoV, especially in Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communications, is to ensure fast, secure transmission and accurate recording of the data. In order to overcome these challenges, this work is adapting Blockchain technology for real time application (RTA) to solve Vehicle-to-Everything (V2X) communications problems. Therefore, the main novelty of this paper is to develop a Blockchain-based IoT system in order to establish secure communication and create an entirely decentralized cloud computing platform. Moreover, the authors qualitatively tested the performance and resilience of the proposed system against common security attacks. Computational tests showed that the proposed solution solved the main challenges of Vehicle-to-X (V2X) communications such as security, centralization, and lack of privacy. In addition, it guaranteed an easy data exchange between different actors of intelligent transportation systems.

Supervisory Decision-Making

2002 ◽  
Vol 46 (3) ◽  
pp. 318-322
Author(s):  
Dwight P. Miller ◽  
Jack Schryver ◽  
Daniel R. Tufano
Keyword(s):  
Decision Making ◽  
Intelligent Transportation Systems ◽  
Large Scale ◽  
Decision Aids ◽  
Organization Design ◽  
Transportation Systems ◽  
Design Guidelines ◽  
Intelligent Software ◽  
Trust In Automation ◽  
Dynamic Decisions

Supervisory Decision-Making (SDM) refers to human supervision of several semi-autonomous (nonhuman) systems in a collaborative manner to accomplish a goal. This study defined SDM and distinguished it from traditional supervisory control and decision-making. An examination of diverse literature in organization design, biology, robotics, innovation diffusion, and trust in automation, yielded no directly applicable or comprehensive models. Field observations were made of large-scale war-games, where operators interacted with semi/autonomous sensors and defense-management systems. Four cognitive models were subsequently developed describing 1) adaptive partnering with automation, 2) technology adoption, 3) trust in automation, and 4) dealing with advice from decision aids. The latter quantitatively models individual, dynamic decisions to accept or reject recommendations made by automated battlespace advisors. The anticipated benefits of this work include more effective human-robot coordination, communication, the identification of experiments, and ultimately design guidelines for robotics, intelligent software agents, intelligent transportation systems, and space exploration.

scholarly journals Edge Computing and IoT Analytics for Agile Optimization in Intelligent Transportation Systems

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6309
Author(s):  
Mohammad Peyman ◽  
Pedro J. Copado ◽  
Rafael D. Tordecilla ◽  
Leandro do C. Martins ◽  
Fatos Xhafa ◽  
...  
Keyword(s):  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Transportation Systems ◽  
Edge Computing ◽  
Analytical Models ◽  
Current Status ◽  
Ride Sharing ◽  
The City

With the emergence of fog and edge computing, new possibilities arise regarding the data-driven management of citizens’ mobility in smart cities. Internet of Things (IoT) analytics refers to the use of these technologies, data, and analytical models to describe the current status of the city traffic, to predict its evolution over the coming hours, and to make decisions that increase the efficiency of the transportation system. It involves many challenges such as how to deal and manage real and huge amounts of data, and improving security, privacy, scalability, reliability, and quality of services in the cloud and vehicular network. In this paper, we review the state of the art of IoT in intelligent transportation systems (ITS), identify challenges posed by cloud, fog, and edge computing in ITS, and develop a methodology based on agile optimization algorithms for solving a dynamic ride-sharing problem (DRSP) in the context of edge/fog computing.These algorithms allow us to process, in real time, the data gathered from IoT systems in order to optimize automatic decisions in the city transportation system, including: optimizing the vehicle routing, recommending customized transportation modes to the citizens, generating efficient ride-sharing and car-sharing strategies, create optimal charging station for electric vehicles and different services within urban and interurban areas. A numerical example considering a DRSP is provided, in which the potential of employing edge/fog computing, open data, and agile algorithms is illustrated.

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