Digital twin, an emerging representation of cyber-physical systems, has attracted increasing attentions very recently. It opens the way to real-time monitoring and synchronization of real-world activities with the virtual counterparts. In this study, we develop a digital twin paradigm using an advanced driver assistance system (ADAS) for connected vehicles. By leveraging vehicle-to-cloud (V2C) communication, on-board devices can up-load the data to the server through cellular network. The server creates a virtual world based on the received data, processes them with the proposed models, and sends them back to the connected vehicles. Drivers can benefit from this V2C based ADAS, even if all computations are conducted on the cloud. The cooperative ramp merging case study is conducted, and the field implementation results show the proposed digital twin framework can benefit the transportation systems regarding mobility and environmental sustainability with acceptable communication delays and packet losses.
LiDAR and Camera Detection Fusion in a Real-Time Industrial Multi-Sensor Collision Avoidance System
Pan Wei * ID , Lucas Cagle, Tasmia Reza, John Ball ID and James Gafford
Center for Advanced Vehicular Systems (CAVS), Mississippi State University, Mississippi State, MS 39759, USA;
SENSOR FUSION: A COMPARISON OF SENSING CAPABILITIES OF HUMAN DRIVERS AND HIGHLY AUTOMATED VEHICLES
BRANDON SCHOETTLE
SWT-2017-12
Multisensor Data Fusion Strategies for Advanced Driver Assistance Systems
Mahdi Rezaei Ghahroudi1 and Reza Sabzevari2
1Department of Computer Engineering, Islamic Azad University of Qazvin
Multiple Sensor Fusion for Detection, Classification and Tracking of Moving Objects in Driving Environments
R. Omar Chavez-Garcia
To cite this version:
Deep Sensor Fusion for ADAS Applications
Vijay John, Seiichi Mita,
Smart Vehicle Research Center
工业4.0创新平台 版权所有 All Rights Reserved, Copyright© 2013- 京ICP备14017844号-3
