Real-time Volume Estimation of Mass in Excavator Bucket with LiDAR Data

2021 ◽  
Author(s):  
Haodong Ding ◽  
Xibin Song ◽  
Zhenpeng He ◽  
Liangjun Zhang
Keyword(s):  
Real Time ◽  
Volume Estimation ◽  
Lidar Data ◽  
Excavator Bucket

Real-Time Highly Resolved Spatial-Temporal Vehicle Energy Consumption Estimation Using Machine Learning and Probe Data

2021 ◽  
pp. 036119812110391
Author(s):  
Joseph Severino ◽  
Yi Hou ◽  
Ambarish Nag ◽  
Jacob Holden ◽  
Lei Zhu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Real Time ◽  
Volume Estimation ◽  
Energy Estimates ◽  
Volume Data ◽  
Road Transportation ◽  
Energy Efficient Routing ◽  
Real World Data ◽  
Consumption Data

Real-time highly resolved spatial-temporal vehicle energy consumption is a key missing dimension in transportation data. Most roadway link-level vehicle energy consumption data are estimated using average annual daily traffic measures derived from the Highway Performance Monitoring System; however, this method does not reflect day-to-day energy consumption fluctuations. As transportation planners and operators are becoming more environmentally attentive, they need accurate real-time link-level vehicle energy consumption data to assess energy and emissions; to incentivize energy-efficient routing; and to estimate energy impact caused by congestion, major events, and severe weather. This paper presents a computational workflow to automate the estimation of time-resolved vehicle energy consumption for each link in a road network of interest using vehicle probe speed and count data in conjunction with machine learning methods in real time. The real-time pipeline can deliver energy estimates within a couple seconds on query to its interface. The proposed method was evaluated on the transportation network of the metropolitan area of Chattanooga, Tennessee. The volume estimation results were validated with ground truth traffic volume data collected in the field. To demonstrate the effectiveness of the proposed method, the energy consumption pipeline was applied to real-world data to quantify road transportation-related energy reduction because of mitigation policies to slow the spread of COVID-19 and to measure energy loss resulting from congestion.

Real-Time Dense Depth Estimation Using Semantically-Guided LIDAR Data Propagation and Motion Stereo

2019 ◽  
Vol 4 (4) ◽  
pp. 3806-3811 ◽  
Author(s):  
Atsuki Hirata ◽  
Ryoichi Ishikawa ◽  
Menandro Roxas ◽  
Takeshi Oishi
Keyword(s):  
Real Time ◽  
Depth Estimation ◽  
Lidar Data ◽  
Data Propagation ◽  
Motion Stereo ◽  
Dense Depth Estimation

scholarly journals Real-time classification of ground from lidar data for helicopter navigation

2013 ◽  
Author(s):  
Ferdinand Eisenkeil ◽  
Tobias Schafhitzel ◽  
Uwe Kühne ◽  
Oliver Deussen
Keyword(s):  
Real Time ◽  
Lidar Data ◽  
Real Time Classification

scholarly journals Design and Implementation of Edge-Fog-Cloud System through HD Map Generation from LiDAR Data of Autonomous Vehicles

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2084
Author(s):  
Junwon Lee ◽  
Kieun Lee ◽  
Aelee Yoo ◽  
Changjoo Moon
Keyword(s):  
Real Time ◽  
Autonomous Vehicles ◽  
Fog Computing ◽  
Point Clouds ◽  
Lidar Data ◽  
High Definition ◽  
Localization And Mapping ◽  
Cloud Server ◽  
Map Generation ◽  
Self Driving Cars

Self-driving cars, autonomous vehicles (AVs), and connected cars combine the Internet of Things (IoT) and automobile technologies, thus contributing to the development of society. However, processing the big data generated by AVs is a challenge due to overloading issues. Additionally, near real-time/real-time IoT services play a significant role in vehicle safety. Therefore, the architecture of an IoT system that collects and processes data, and provides services for vehicle driving, is an important consideration. In this study, we propose a fog computing server model that generates a high-definition (HD) map using light detection and ranging (LiDAR) data generated from an AV. The driving vehicle edge node transmits the LiDAR point cloud information to the fog server through a wireless network. The fog server generates an HD map by applying the Normal Distribution Transform-Simultaneous Localization and Mapping(NDT-SLAM) algorithm to the point clouds transmitted from the multiple edge nodes. Subsequently, the coordinate information of the HD map generated in the sensor frame is converted to the coordinate information of the global frame and transmitted to the cloud server. Then, the cloud server creates an HD map by integrating the collected point clouds using coordinate information.

Accuracy of real-time, three-dimensional Doppler echocardiography for stroke volume estimation compared with phase-encoded MRI: an in vivo study

Heart ◽  
2008 ◽  
Vol 94 (9) ◽  
pp. 1212-1213 ◽  
Author(s):  
J Pemberton ◽  
M Jerosch-Herold ◽  
X Li ◽  
L Hui ◽  
M Silberbach ◽  
...  
Keyword(s):  
Stroke Volume ◽  
Real Time ◽  
Doppler Echocardiography ◽  
Three Dimensional ◽  
Volume Estimation ◽  
In Vivo Study
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