Parallel control and management have been proposed as a new mechanism for conducting operations of complex systems, especially those that involved complexity issues of both engineering and social ...dimensions, such as transportation systems. This paper presents an overview of the background, concepts, basic methods, major issues, and current applications of Parallel transportation Management Systems (PtMS). In essence, parallel control and management is a data-driven approach for modeling, analysis, and decision-making that considers both the engineering and social complexity in its processes. The developments and applications described here clearly indicate that PtMS is effective for use in networked complex traffic systems and is closely related to emerging technologies in cloud computing, social computing, and cyberphysical-social systems. A description of PtMS system architectures, processes, and components, including OTSt, Dyna CAS, aDAPTS, iTOP, and TransWorld is presented and discussed. Finally, the experiments and examples of real-world applications are illustrated and analyzed.
Briefing: An investigation and outline of MetaControl and DeControl in Metaverses for control intelligence and knowledge automation are presented. Prescriptive control with prescriptive knowledge and ...parallel philosophy is proposed as the starting point for the new control philosophy and technology, especially for computational control of metasystems in cyber-physical-social systems. We argue that circular causality, the generalized feedback mechanism for complex and purposive systems, should be adapted as the fundamental principle for control and management of metasystems with metacomplexity in metaverses. Particularly, an interdisciplinary approach is suggested for MetaControl and DeControl as a new form of intelligent control based on five control metaverses: Meta Verses, MultiVerses, InterVerses, TransVerse, and Deep Verses.
When IEEE Intelligent Systems solicited ideas for a new department, cyberphysical systems(CPS) received overwhelming support.Cyber-Physical-Social Systems is the new name for CPS. CPSS is the ...enabling platform technology that will lead us to an era of intelligent enterprises and industries. Internet use and cyberspace activities have created an overwhelming demand for the rapid development and application of CPSS. CPSS must be conducted with a multidisciplinary approach involving the physical, social, and cognitive sciences and that Al-based intelligent systems will be key to any successful construction and deployment.
Driver decisions and behaviors are essential factors that can affect the driving safety. To understand the driver behaviors, a driver activities recognition system is designed based on the deep ...convolutional neural networks (CNN) in this paper. Specifically, seven common driving activities are identified, which are the normal driving, right mirror checking, rear mirror checking, left mirror checking, using in-vehicle radio device, texting, and answering the mobile phone, respectively. Among these activities, the first four are regarded as normal driving tasks, while the rest three are classified into the distraction group. The experimental images are collected using a low-cost camera, and ten drivers are involved in the naturalistic data collection. The raw images are segmented using the Gaussian mixture model to extract the driver body from the background before training the behavior recognition CNN model. To reduce the training cost, transfer learning method is applied to fine tune the pre-trained CNN models. Three different pre-trained CNN models, namely, AlexNet, GoogLeNet, and ResNet50 are adopted and evaluated. The detection results for the seven tasks achieved an average of 81.6% accuracy using the AlexNet, 78.6% and 74.9% accuracy using the GoogLeNet and ResNet50, respectively. Then, the CNN models are trained for the binary classification task and identify whether the driver is being distracted or not. The binary detection rate achieved 91.4% accuracy, which shows the advantages of using the proposed deep learning approach. Finally, the real-world application are analyzed and discussed.
Intelligent vehicles and advanced driver assistance systems (ADAS) need to have proper awareness of the traffic context, as well as the driver status since ADAS share the vehicle control authorities ...with the human driver. This paper provides an overview of the ego-vehicle driver intention inference (DII), which mainly focuses on the lane change intention on highways. First, a human intention mechanism is discussed in the beginning to gain an overall understanding of the driver intention. Next, the ego-vehicle driver intention is classified into different categories based on various criteria. A complete DII system can be separated into different modules, which consist of traffic context awareness, driver states monitoring, and the vehicle dynamic measurement module. The relationship between these modules and the corresponding impacts on the DII are analyzed. Then, the lane change intention inference system is reviewed from the perspective of input signals, algorithms, and evaluation. Finally, future concerns and emerging trends in this area are highlighted.
Do we need a fundamental change in our professional culture and knowledge foundation for control and automation? If so, what are necessary and critical steps we must take to ensure such a change ...would take place effectively and efficiently, or more general, smoothly and sustainably?
A Survey of Traffic Data Visualization Chen, Wei; Guo, Fangzhou; Wang, Fei-Yue
IEEE transactions on intelligent transportation systems,
12/2015, Volume:
16, Issue:
6
Journal Article
Peer reviewed
Data-driven intelligent transportation systems utilize data resources generated within intelligent systems to improve the performance of transportation systems and provide convenient and reliable ...services. Traffic data refer to datasets generated and collected on moving vehicles and objects. Data visualization is an efficient means to represent distributions and structures of datasets and reveal hidden patterns in the data. This paper introduces the basic concept and pipeline of traffic data visualization, provides an overview of related data processing techniques, and summarizes existing methods for depicting the temporal, spatial, numerical, and categorical properties of traffic data.
In this paper, we study the difference between two major strategies of cooperative driving at nonsignalized intersections: namely the "ad hoc negotiation-based" strategy and the "planning-based" ...strategy. The fundamental divide of these two strategies lies in how to determine the passing order of vehicles at intersections. The "ad hoc negotiation-based" strategy makes vehicles roughly follow first-come-first-served order but allows some adjustments. This leads to a local optimal solution in many situations. The "planning-based" strategy aims to find a global optimal passing order of vehicles. However, constrained by the planning complexity and time requirement, we often stop at a local optimal solution, too. We carry out a series of simulations to compare the solutions found by two strategies, under different traffic scenarios. Results indicate the performance of a strategy mainly depends on the passing order of vehicles that it finds. Although there exist several trajectory planning algorithms associating with the solving process of passing orders, their differences are negligible. Moreover, if the traffic demand is very low, the performance difference between two strategies is small. When the traffic demand becomes high, the "planning-based" strategy yields significantly better performance since it finds better passing orders. These findings are important to cooperative driving study.
Traffic data imputation is critical for both research and applications of intelligent transportation systems. To develop traffic data imputation models with high accuracy, traffic data must be large ...and diverse, which is costly. An alternative is to use synthetic traffic data, which is cheap and easy-access. In this paper, we propose a novel approach using parallel data and generative adversarial networks (GANs) to enhance traffic data imputation. Parallel data is a recently proposed method of using synthetic and real data for data mining and data-driven process, in which we apply GANs to generate synthetic traffic data. As it is difficult for the standard GAN algorithm to generate time-dependent traffic flow data, we made twofold modifications: 1) using the real data or the corrupted ones instead of random vectors as latent codes to generator within GANs and 2) introducing a representation loss to measure discrepancy between the synthetic data and the real data. The experimental results on a real traffic dataset demonstrate that our method can significantly improve the performance of traffic data imputation.
As an emerging decentralized architecture and distributed computing paradigm underlying Bitcoin and other cryptocurrencies, blockchain has attracted intensive attention in both research and ...applications in recent years. The key advantage of this technology lies in the fact that it enables the establishment of secured, trusted, and decentralized autonomous ecosystems for various scenarios, especially for better usage of the legacy devices, infrastructure, and resources. In this paper, we presented a systematic investigation of blockchain and cryptocurrencies. Related fundamental rationales, technical advantages, existing and potential ecosystems of Bitcoin and other cryptocurrencies are discussed, and a six-layer reference model of the blockchain framework is proposed with detailed description for each of its six layers. Potential applications of blockchain and cryptocurrencies are also addressed. Our aim here is to provide guidance and reference for future research along this promising and important direction.
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