Intelligent connected vehicle cloud data platform for park scene

2021 ◽  
Author(s):  
Maoyuan Cui ◽  
Yanxi Gao ◽  
Huiqin Zhan ◽  
ZhongLin Luo ◽  
RunFa Zhou ◽  
...  
Keyword(s):  
Connected Vehicle ◽  
Cloud Data ◽  
Data Platform

scholarly journals On-Board Data Management Layer: Connected Vehicle as Data Platform

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1810
Author(s):  
Khireddine Benaissa ◽  
Salim Bitam ◽  
Abdelhamid Mellouk
Keyword(s):  
Data Management ◽  
Data Science ◽  
Connected Vehicle ◽  
Embedded Computing ◽  
Traffic Flow Prediction ◽  
Vehicle Data ◽  
Data Platform ◽  
Dataset Size ◽  
Computing Platforms ◽  
Additional Value

For connected vehicles, as well as generally for the transportation sector, data are now seen as a precious resource. They can be used to make right decisions, improve road safety, reduce CO2 emissions, or optimize processes. However, analyzing these data is not so much a question of which technologies to use, but rather about where these data are analyzed. Thereby, the emerging vehicle architecture has to become a data-oriented architecture based on embedded computing platforms and take into account new applications, artificial intelligence elements, advanced analytics, and operating systems. Accordingly, in this paper, we introduce the concept of data management to the vehicle by proposing an on-board data management layer, so that the vehicle can play the role of data platform capable of storing, processing, and diffusing data. Our proposed layer supports analytics and data science to deliver additional value from the connected vehicle data and stimulate the development of new services. In addition, our data platform can also form or contribute to shaping the backbone of data-driven transport. An on-board platform was built where the dataset size was reduced 80% and a rate of 99% accuracy was achieved in a 5 min traffic flow prediction using artificial neural networks (ANNs).

scholarly journals Smart Agro E-Marketplace Architectural Model Based on Cloud Data Platform

2021 ◽  
Vol 1874 (1) ◽  
pp. 012022
Author(s):  
Khairul Anwar Sedek ◽  
Mohd Nizam Osman ◽  
Mohd Adib Omar ◽  
Mohd Helmy Abdul Wahab ◽  
Syed Zulkarnain Syed Idrus
Keyword(s):  
Cloud Data ◽  
Model Based ◽  
Data Platform ◽  
Architectural Model

Control Model Constructing on Real-Time Dynamic Reversible Lane in Condition of Connected Vehicle

CICTP 2020 ◽  
2020 ◽  
Author(s):  
Lina Mao ◽  
Wenquan Li ◽  
Pengsen Hu ◽  
Guiliang Zhou ◽  
Huiting Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Control Model ◽  
Connected Vehicle ◽  
Time Dynamic

Registration and Fusion of UAV LiDAR System Sequence Images and Laser Point Clouds

2020 ◽  
Author(s):  
Jiayong Yu ◽  
Longchen Ma ◽  
Maoyi Tian, ◽  
Xiushan Lu
Keyword(s):  
Point Cloud ◽  
Image Data ◽  
Point Clouds ◽  
Optical Image ◽  
Point Cloud Data ◽  
Feature Points ◽  
Lidar System ◽  
Registration Accuracy ◽  
Cloud Data ◽  
Intensity Image

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

Sign in / Sign up

Export Citation Format

Share Document