Lessons Learned from the Real-World Deployment of a Connected Vehicle Testbed

The connected vehicle (CV) system promises unprecedented safety, mobility, environmental, economic, and social benefits, which can be unlocked using the enormous amount of data shared between vehicles and infrastructure (e.g., traffic signals, centers). Real-world CV deployments, including pilot deployments, help solve technical issues and observe potential benefits, both of which support the broader adoption of the CV system. This study focused on the Clemson University Connected Vehicle Testbed (CU-CVT) with the goal of sharing the lessons learned from the CU-CVT deployment. The motivation of this study was to enhance early CV deployments with the objective of depicting the lessons learned from the CU-CVT testbed, which includes unique features to support multiple CV applications running simultaneously. The lessons learned in the CU-CVT testbed are described at three different levels: (i) the development of system architecture and prototyping in a controlled environment, (ii) the deployment of the CU-CVT testbed, and (iii) the validation of the CV application experiments in the CU-CVT. Field experiments with CV applications validated the functionalities needed for running multiple diverse CV applications simultaneously using heterogeneous wireless networking, and meeting real-time and non-real-time application requirements. The unique deployment experiences, related to heterogeneous wireless networks, real-time data aggregation, data dissemination and processing using a broker system, and data archiving with big data management tools, gained from the CU-CVT testbed, could be used to advance CV research and guide public and private agencies for the deployment of CVs in the real world.

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Automatic Ground Points Identification Method for Roadside LiDAR Data

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198119843869 ◽

2019 ◽

Vol 2673 (6) ◽

pp. 140-152 ◽

Cited By ~ 5

Author(s):

Jianqing Wu ◽

Hao Xu ◽

Bin Lv ◽

Rui Yue ◽

Yang Li

Keyword(s):

Data Processing ◽

Real Time ◽

Transition Period ◽

Ground Surface ◽

Lidar Data ◽

Connected Vehicle ◽

Time Data ◽

The Real ◽

Traffic Volumes ◽

Real Time Data Processing

Roadside light detection and ranging (LiDAR) provides a solution to fill the data gap under mixed traffic situations. The real-time high-resolution micro traffic data (HRMTD) of all road users from the roadside LiDAR sensor provides a new opportunity to serve the connected-vehicle system during the transition period from unconnected vehicles to connected vehicles. Ground surface identification is the basic data processing step for HRMTD collection. The current ground points identification algorithms based on airborne and mobile LiDAR do not work for roadside LiDAR. A novel algorithm is developed in this paper to identify and exclude ground points based on the features of LiDAR, terrain, and point density in the space. The scan feature of different beams is used to search ground points. The whole procedure can be divided into four major parts: points clustering in each beam, slope-based filtering, shape-based filtering, and ground points matrix extraction. The proposed algorithm was evaluated using the real-world LiDAR data collected at different scenarios. The results showed that this algorithm can be used for ground points exclusion under different situations (differing terrain types, weather situations, and traffic volumes) with high accuracy. This algorithm was compared with previously developed algorithms. The overall performance of the proposed algorithm is superior. The low computational load guarantees this method may be applied for real-time data processing.

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Understanding Phenomena in the Real World: The Case for Real Time Data Collection in Health Services Research

Journal of Health Services Research & Policy ◽

10.1258/jhsrp.2010.010016 ◽

2011 ◽

Vol 16 (3) ◽

pp. 172-176 ◽

Cited By ~ 20

Author(s):

Martyn Jones ◽

Derek Johnston

Keyword(s):

Data Collection ◽

Health Services Research ◽

Health Services ◽

Real Time ◽

Real World ◽

Time Data ◽

Services Research ◽

The Real ◽

Real Time Data

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The Importance of Implementing Cyber Physical Systems to Acquire Real-Time Data and Indicators

J ◽

10.3390/j4020012 ◽

2021 ◽

Vol 4 (2) ◽

pp. 147-153

Author(s):

Paula Morella ◽

María Pilar Lambán ◽

Jesús Antonio Royo ◽

Juan Carlos Sánchez

Keyword(s):

Decision Making ◽

Data Acquisition ◽

Real Time ◽

Failure Detection ◽

Cyber Physical Systems ◽

Future Research ◽

Time Data ◽

Physical Systems ◽

The Real ◽

Real Time Data

Among the new trends in technology that have emerged through the Industry 4.0, Cyber Physical Systems (CPS) and Internet of Things (IoT) are crucial for the real-time data acquisition. This data acquisition, together with its transformation in valuable information, are indispensable for the development of real-time indicators. Moreover, real-time indicators provide companies with a competitive advantage over the competition since they enhance the calculus and speed up the decision-making and failure detection. Our research highlights the advantages of real-time data acquisition for supply chains, developing indicators that would be impossible to achieve with traditional systems, improving the accuracy of the existing ones and enhancing the real-time decision-making. Moreover, it brings out the importance of integrating technologies 4.0 in industry, in this case, CPS and IoT, and establishes the main points for a future research agenda of this topic.

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The real-time big data processing method based on LSTM or GRU for the smart job shop production process

Journal of Algorithms & Computational Technology ◽

10.1177/1748302620962390 ◽

2020 ◽

Vol 14 ◽

pp. 174830262096239 ◽

Cited By ~ 1

Author(s):

Chuang Wang ◽

Wenbo Du ◽

Zhixiang Zhu ◽

Zhifeng Yue

Keyword(s):

Big Data ◽

Data Processing ◽

Production Process ◽

Real Time ◽

Job Shop ◽

Production Data ◽

Time Data ◽

Big Data Processing ◽

The Real ◽

Data Processing Method

With the wide application of intelligent sensors and internet of things (IoT) in the smart job shop, a large number of real-time production data is collected. Accurate analysis of the collected data can help producers to make effective decisions. Compared with the traditional data processing methods, artificial intelligence, as the main big data analysis method, is more and more applied to the manufacturing industry. However, the ability of different AI models to process real-time data of smart job shop production is also different. Based on this, a real-time big data processing method for the job shop production process based on Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) is proposed. This method uses the historical production data extracted by the IoT job shop as the original data set, and after data preprocessing, uses the LSTM and GRU model to train and predict the real-time data of the job shop. Through the description and implementation of the model, it is compared with KNN, DT and traditional neural network model. The results show that in the real-time big data processing of production process, the performance of the LSTM and GRU models is superior to the traditional neural network, K nearest neighbor (KNN), decision tree (DT). When the performance is similar to LSTM, the training time of GRU is much lower than LSTM model.

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Real-Time Unspecified Major Sub-Events Detection in the Twitter Data Stream That Cause the Change in the Sentiment Score of the Targeted Event

International Journal of Information Technology and Web Engineering ◽

10.4018/ijitwe.2017100101 ◽

2017 ◽

Vol 12 (4) ◽

pp. 1-21 ◽

Cited By ~ 10

Author(s):

Ritesh Srivastava ◽

M.P.S. Bhatia

Keyword(s):

Real Time ◽

Real World ◽

Virtual Communities ◽

Change Points ◽

Time Analysis ◽

The Real ◽

Real Time Analysis ◽

Twitter Data ◽

Sentiment Score ◽

Events Detection

Twitter behaves as a social sensor of the world. The tweets provided by the Twitter Firehose reveal the properties of big data (i.e. volume, variety, and velocity). With millions of users on Twitter, the Twitter's virtual communities are now replicating the real-world communities. Consequently, the discussions of real world events are also very often on Twitter. This work has performed the real-time analysis of the tweets related to a targeted event (e.g. election) to identify those potential sub-events that occurred in the real world, discussed over Twitter and cause the significant change in the aggregated sentiment score of the targeted event with time. Such type of analysis can enrich the real-time decision-making ability of the event bearer. The proposed approach utilizes a three-step process: (1) Real-time sentiment analysis of tweets (2) Application of Bayesian Change Points Detection to determine the sentiment change points (3) Major sub-events detection that have influenced the sentiment of targeted event. This work has experimented on Twitter data of Delhi Election 2015.

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Augmented Reality Berbasis Android Sebagai Media Pembelajaran Sistem Tata Surya

Jurnal CoSciTech (Computer Science and Information Technology) ◽

10.37859/coscitech.v2i1.2178 ◽

2021 ◽

Vol 2 (1) ◽

pp. 53-59

Author(s):

Yulia Fatma ◽

Armen Salim ◽

Regiolina Hayami

Keyword(s):

Augmented Reality ◽

Solar System ◽

Real Time ◽

Real World ◽

Virtual World ◽

The Real ◽

Virtual Objects ◽

Augmented Reality Technology ◽

The Revolution

Along with the development, the application can be used as a medium for learning. Augmented Reality is a technology that combines two-dimensional’s virtual objects and three-dimensional’s virtual objects into a real three-dimensional’s then projecting the virtual objects in real time and simultaneously. The introduction of Solar System’s material, students are invited to get to know the planets which are directly encourage students to imagine circumtances in the Solar System. Explenational of planets form and how the planets make the revolution and rotation in books are considered less material’s explanation because its only display objects in 2D. In addition, students can not practice directly in preparing the layout of the planets in the Solar System. By applying Augmented Reality Technology, information’s learning delivery can be clarified, because in these applications are combined the real world and the virtual world. Not only display the material, the application also display images of planets in 3D animation’s objects with audio.

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A Real-Time Weed Mapping and Precision Herbicide Spraying System for Row Crops

Sensors ◽

10.3390/s18124245 ◽

2018 ◽

Vol 18 (12) ◽

pp. 4245 ◽

Cited By ~ 4

Author(s):

Yanlei Xu ◽

Zongmei Gao ◽

Lav Khot ◽

Xiaotian Meng ◽

Qin Zhang

Keyword(s):

Real Time ◽

Field Experiments ◽

Particle Swarm ◽

Error Rates ◽

Integrated System ◽

Central Processor ◽

Time Data ◽

Weed Detection ◽

Row Crops ◽

Weed Mapping

This study developed and field tested an automated weed mapping and variable-rate herbicide spraying (VRHS) system for row crops. Weed detection was performed through a machine vision sub-system that used a custom threshold segmentation method, an improved particle swarm optimum (IPSO) algorithm, capable of segmenting the field images. The VRHS system also used a lateral histogram-based algorithm for fast extraction of weed maps. This was the basis for determining real-time herbicide application rates. The central processor of the VRHS system had high logic operation capacity, compared to the conventional controller-based systems. Custom developed monitoring system allowed real-time visualization of the spraying system functionalities. Integrated system performance was then evaluated through field experiments. The IPSO successfully segmented weeds within corn crop at seedling growth stage and reduced segmentation error rates to 0.1% from 7.1% of traditional particle swarm optimization algorithm. IPSO processing speed was 0.026 s/frame. The weed detection to chemical actuation response time of integrated system was 1.562 s. Overall, VRHS system met the real-time data processing and actuation requirements for its use in practical weed management applications.

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