scholarly journals PC5-Based Cellular-V2X Evolution and Deployment

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 843
Author(s):  
Lili Miao ◽  
John Jethro Virtusio ◽  
Kai-Lung Hua
Keyword(s):  
Mobile Networks ◽  
Intelligent Transportation Systems ◽  
Large Scale ◽  
High Reliability ◽  
Cost Benefit ◽  
Autonomous Driving ◽  
Transportation Systems ◽  
Road User ◽  
Low Latency ◽  
Radio Communications

C-V2X (Cellular Vehicle-to-Everything) is a state-of-the-art wireless technology used in autonomous driving and intelligent transportation systems (ITS). This technology has extended the coverage and blind-spot detection of autonomous driving vehicles. Economically, C-V2X is much more cost-effective than the traditional sensors that are commonly used by autonomous driving vehicles. This cost-benefit makes it more practical in a large scale deployment. PC5-based C-V2X uses an RF (Radio Frequency) sidelink direct communication for low latency mission-critical vehicle sensor connectivity. Over the C-V2X radio communications, the autonomous driving vehicle’s sensor ability can now be largely enhanced to the distances as far as the network covers. In 2020, 5G is commercialized worldwide, and Taiwan is at the forefront. Operators and governments are keen to see its implications in people’s daily life brought by its low latency, high reliability, and high throughput. Autonomous driving class L3 (Conditional Automation) or L4 (Highly Automation) are good examples of 5G’s advanced applications. In these applications, the mobile networks with URLLC (Ultra-Reliable Low-Latency Communication) are perfectly demonstrated. Therefore, C-V2X evolution and 5G NR (New Radio) deployment coincide and form a new ecosystem. This ecosystem will change how people will drive and how transportation will be managed in the future. In this paper, the following topics are covered. Firstly, the benefits of C-V2X communication technology. Secondly, the standards of C-V2X and C-V2X applications for automotive road safety system which includes V2P/V2I/V2V/V2N, and artificial intelligence in VRU (Vulnerable Road User) detection, object recognition and movement prediction for collision warning and prevention. Thirdly, PC5-based C-V2X deployment status in global, especially in Taiwan. Lastly, current challenges and conclusions of C-V2X development.

scholarly journals Latency reduction for narrowband URLLC networks: a performance evaluation

2021 ◽  
Author(s):  
Zubair Amjad ◽  
Kofi Atta Nsiah ◽  
Benoît Hilt ◽  
Jean-Philippe Lauffenburger ◽  
Axel Sikora
Keyword(s):  
Cellular Networks ◽  
Mobile Networks ◽  
Intelligent Transportation Systems ◽  
Transportation Systems ◽  
Fourth Generation ◽  
Low Latency ◽  
Time Interval ◽  
Latency Reduction ◽  
Reduction Techniques ◽  
Application Fields

AbstractFifth-generation (5G) cellular mobile networks are expected to support mission-critical low latency applications in addition to mobile broadband services, where fourth-generation (4G) cellular networks are unable to support Ultra-Reliable Low Latency Communication (URLLC). However, it might be interesting to understand which latency requirements can be met with both 4G and 5G networks. In this paper, we discuss (1) the components contributing to the latency of cellular networks and (2) evaluate control-plane and user-plane latencies for current-generation narrowband cellular networks and point out the potential improvements to reduce the latency of these networks, (3) present, implement and evaluate latency reduction techniques for latency-critical applications. The two elements we detected, namely the short transmission time interval and the semi-persistent scheduling are very promising as they allow to shorten the delay to processing received information both into the control and data planes. We then analyze the potential of latency reduction techniques for URLLC applications. To this end, we develop these techniques into the long term evolution (LTE) module of ns-3 simulator and then evaluate the performance of the proposed techniques into two different application fields: industrial automation and intelligent transportation systems. Our detailed evaluation results from simulations indicate that LTE can satisfy the low-latency requirements for a large choice of use cases in each field.

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 DetReco: Object-Text Detection and Recognition Based on Deep Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Fan Zhang ◽  
Jiaxing Luan ◽  
Zhichao Xu ◽  
Wei Chen
Keyword(s):  
Intelligent Transportation Systems ◽  
Research Effort ◽  
Recognition Task ◽  
Autonomous Driving ◽  
Transportation Systems ◽  
Text Detection ◽  
Text Recognition ◽  
Test Time ◽  
Average Precision ◽  
Detection And Recognition

Deep learning-based object detection method has been applied in various fields, such as ITS (intelligent transportation systems) and ADS (autonomous driving systems). Meanwhile, text detection and recognition in different scenes have also attracted much attention and research effort. In this article, we propose a new object-text detection and recognition method termed “DetReco” to detect objects and texts and recognize the text contents. The proposed method is composed of object-text detection network and text recognition network. YOLOv3 is used as the algorithm for the object-text detection task and CRNN is employed to deal with the text recognition task. We combine the datasets of general objects and texts together to train the networks. At test time, the detection network detects various objects in an image. Then, the text images are passed to the text recognition network to derive the text contents. The experiments show that the proposed method achieves 78.3 mAP (mean Average Precision) for general objects and 72.8 AP (Average Precision) for texts in regard to detection performance. Furthermore, the proposed method is able to detect and recognize affine transformed or occluded texts with robustness. In addition, for the texts detected around general objects, the text contents can be used as the identifier to distinguish the object.

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.

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 GNSS RUMS: GNSS Realistic Urban Multi-agent Simulator for Collaborative Positioning Research

2020 ◽  
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 ◽  
Multipath Signal ◽  
Multi Agent ◽  
Scale Test ◽  
Error Behavior

Accurate localization of road agents 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 are 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 experiment requiring numbers of devices is hard to be conducted, 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, carrier-phase, 〖C/N〗_0, and Doppler shift measurements correspondingly. The performance of the proposed simulator is validated through real experimental comparisons with different scenarios. 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.

New Approach to Transportation Planning for the 21st Century

2005 ◽  
Vol 1931 (1) ◽  
pp. 89-98 ◽  
Author(s):  
Joseph M. Sussman ◽  
Sgouris P. Sgouridis ◽  
John L. Ward
Keyword(s):  
Intelligent Transportation Systems ◽  
Transportation Planning ◽  
Large Scale ◽  
New Technologies ◽  
Open Systems ◽  
Regional Scale ◽  
Transportation Systems ◽  
Cultural Perspectives ◽  
Institutional Issues ◽  
Operations And Maintenance

The complex large-scale integrated open systems (CLIOS) process is an overarching mechanism for considering systems, especially those exhibiting nested complexity, in which both the physical and the institutional aspects are complex. A special case of the CLIOS process is regional strategic transportation planning. New technologies, such as intelligent transportation systems, allow consideration of the planning, management, operations, and maintenance of transportation systems at the regional scale. Although the technological issues in advancing to this scale have proved tractable, the institutional issues concerned with deploying these systems across political jurisdictions with different measures of performance and different cultural perspectives has proved quite difficult. This paper explores processes for studying these institutional questions and integrating a number of concepts into the process: technological change, sustainability, real options, supply chain management, the various transport modes, and social, political, and economic factors. The paper serves as a case study of tailoring the CLIOS process into a process specifically designed to systematically address particular issues, in this case, regional strategic transportation planning.

Clustering, Connectivity, and Monitoring Challenges in Vehicular Ad hoc Networks

2010 ◽  
pp. 257-281
Author(s):  
Ameneh Daeinabi ◽  
Akbar Ghaffarpour Rahbar
Keyword(s):  
Ad Hoc Networks ◽  
Intelligent Transportation Systems ◽  
Large Scale ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Clustering Algorithms ◽  
Transportation Systems ◽  
High Rate ◽  
Efficient Resource ◽  
Hoc Networks

Vehicular Ad Hoc Networks (VANETs) are appropriate networks that can be applied for intelligent transportation systems. Three important challenges in VANETs are studied in this chapter. The first challenge is to defend against attackers. Because of the lack of a coordination unit in a VANET, vehicles should cooperate together and monitor each other in order to enhance security performance of the VANET. As the second challenge in VANETs, scalability is a critical issue for a network designer. Clustering is one solution for the scalability problem and is vital for efficient resource consumption and load balancing in large scale VANETs. On the other hand, due to the high-rate topology changes and high variability in vehicles density, transmission range of a vehicle is an important issue for forwarding and receiving messages. In this chapter, we study the clustering algorithms, the solutions appropriate to increase connectivity, and the algorithms that can detect attackers in a VANET.

scholarly journals OLIMP: A Heterogeneous Multimodal Dataset for Advanced Environment Perception

Electronics ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 560 ◽  
Author(s):  
Amira Mimouna ◽  
Ihsen Alouani ◽  
Anouar Ben Khalifa ◽  
Yassin El Hillali ◽  
Abdelmalik Taleb-Ahmed ◽  
...  
Keyword(s):  
Intelligent Transportation Systems ◽  
Weather Conditions ◽  
Autonomous Driving ◽  
Radar Data ◽  
Transportation Systems ◽  
Urban Traffic ◽  
Future Research ◽  
Acoustic Data ◽  
Cluttered Environment ◽  
Environment Perception

A reliable environment perception is a crucial task for autonomous driving, especially in dense traffic areas. Recent improvements and breakthroughs in scene understanding for intelligent transportation systems are mainly based on deep learning and the fusion of different modalities. In this context, we introduce OLIMP: A heterOgeneous Multimodal Dataset for Advanced EnvIronMent Perception. This is the first public, multimodal and synchronized dataset that includes UWB radar data, acoustic data, narrow-band radar data and images. OLIMP comprises 407 scenes and 47,354 synchronized frames, presenting four categories: pedestrian, cyclist, car and tram. The dataset includes various challenges related to dense urban traffic such as cluttered environment and different weather conditions. To demonstrate the usefulness of the introduced dataset, we propose a fusion framework that combines the four modalities for multi object detection. The obtained results are promising and spur for future research.

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