Real-time RADAR and LIDAR Sensor Fusion for Automated Driving

2020 ◽  
pp. 137-147
Author(s):  
Rohini Devagiri ◽  
Nalini C. Iyer ◽  
Shruti Maralappanavar
Keyword(s):  
Real Time ◽  
Sensor Fusion ◽  
Automated Driving

scholarly journals Assessments of Real-Time Communications over TSN Automotive Networks

Electronics ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 556
Author(s):  
Lucia Lo Bello ◽  
Gaetano Patti ◽  
Giancarlo Vasta
Keyword(s):  
Performance Evaluation ◽  
Real Time ◽  
Communication System ◽  
Traffic Management ◽  
Mac Layer ◽  
Automated Driving ◽  
Real Time Traffic ◽  
Management Scheme ◽  
Event Driven ◽  
Automotive Networks

The IEEE 802.1Q-2018 standard embeds in Ethernet bridges novel features that are very important for automated driving, such as the support for time-driven communications. However, cars move in a world where unpredictable events may occur and determine unforeseen situations. To properly react to such situations, the in-car communication system has to support event-driven transmissions with very low and bounded delays. This work provides the performance evaluation of EDSched, a traffic management scheme for IEEE 802.1Q bridges and end nodes that introduces explicit support for event-driven real-time traffic. EDSched works at the MAC layer and builds upon the mechanisms defined in the IEEE 802.1Q-2018 standard.

Sequential Sensor Fusion-Based Real-Time LSTM Gait Pattern Controller for Biped Robot

2021 ◽  
Vol 21 (2) ◽  
pp. 2241-2255 ◽  
Author(s):  
Tzuu-Hseng S. Li ◽  
Ping-Huan Kuo ◽  
Chuan-Han Cheng ◽  
Chia-Ching Hung ◽  
Po-Chien Luan ◽  
...  
Keyword(s):  
Real Time ◽  
Sensor Fusion ◽  
Biped Robot ◽  
Gait Pattern

Real-time 2D height mapping method for an unmanned vehicle using a stereo camera and laser sensor fusion

2012 ◽  
Vol 10 (4) ◽  
pp. 761-771 ◽  
Author(s):  
Jeong-Hyun Kim ◽  
Young-Zhu Yoon ◽  
Sun-Cheol Yu ◽  
Dong-Joong Kang
Keyword(s):  
Real Time ◽  
Sensor Fusion ◽  
Mapping Method ◽  
Laser Sensor ◽  
Unmanned Vehicle ◽  
Stereo Camera

scholarly journals COVIDSAVIOR: A Novel Sensor-Fusion and Deep Learning Based Framework for Virus Outbreaks

2021 ◽  
Vol 9 ◽  
Author(s):  
Sharnil Pandya ◽  
Anirban Sur ◽  
Nitin Solke
Keyword(s):  
Deep Learning ◽  
Body Temperature ◽  
Real Time ◽  
Sensor Fusion ◽  
Face Mask ◽  
Temperature Threshold ◽  
Mask Condition ◽  
Essential Information ◽  
The Individual ◽  
Scanning Mechanism

The presented deep learning and sensor-fusion based assistive technology (Smart Facemask and Thermal scanning kiosk) will protect the individual using auto face-mask detection and auto thermal scanning to detect the current body temperature. Furthermore, the presented system also facilitates a variety of notifications, such as an alarm, if an individual is not wearing a mask and detects thermal temperature beyond the standard body temperature threshold, such as 98.6°F (37°C). Design/methodology/approach—The presented deep Learning and sensor-fusion-based approach can also detect an individual in with or without mask situations and provide appropriate notification to the security personnel by raising the alarm. Moreover, the smart tunnel is also equipped with a thermal sensing unit embedded with a camera, which can detect the real-time body temperature of an individual concerning the prescribed body temperature limits as prescribed by WHO reports. Findings—The investigation results validate the performance evaluation of the presented smart face-mask and thermal scanning mechanism. The presented system can also detect an outsider entering the building with or without mask condition and be aware of the security control room by raising appropriate alarms. Furthermore, the presented smart epidemic tunnel is embedded with an intelligent algorithm that can perform real-time thermal scanning of an individual and store essential information in a cloud platform, such as Google firebase. Thus, the proposed system favors society by saving time and helps in lowering the spread of coronavirus.

The performance of a real time LAN architecture for sensor fusion applications

2003 ◽  
Author(s):  
V. Cherkassky ◽  
H. Lari-Najaffi ◽  
N.L. Lawrie ◽  
D. Masson ◽  
D.W. Pritty
Keyword(s):  
Real Time ◽  
Sensor Fusion ◽  
Fusion Applications
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