An Expandable Multi-Sensor Data-Fusion Concept for Autonomous Driving in Urban Environments

2007 ◽  
Vol 4 (12) ◽  
pp. 1108-1116 ◽  
Author(s):  
J. Effertz
Keyword(s):  
Data Fusion ◽  
Autonomous Driving ◽  
Urban Environments ◽  
Sensor Data ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion ◽  
Fusion Concept

scholarly journals An improved traffic lights recognition algorithm for autonomous driving in complex scenarios

2021 ◽  
Vol 17 (5) ◽  
pp. 155014772110183
Author(s):  
Ziyue Li ◽  
Qinghua Zeng ◽  
Yuchao Liu ◽  
Jianye Liu ◽  
Lin Li
Keyword(s):  
Data Fusion ◽  
Autonomous Driving ◽  
Recognition Algorithm ◽  
Sensor Data ◽  
Optimal Size ◽  
Data Set ◽  
Traffic Lights ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion ◽  
Public Data

Image recognition is susceptible to interference from the external environment. It is challenging to accurately and reliably recognize traffic lights in all-time and all-weather conditions. This article proposed an improved vision-based traffic lights recognition algorithm for autonomous driving, integrating deep learning and multi-sensor data fusion assist (MSDA). We introduce a method to obtain the best size of the region of interest (ROI) dynamically, including four aspects. First, based on multi-sensor data (RTK BDS/GPS, IMU, camera, and LiDAR) acquired in a normal environment, we generated a prior map that contained sufficient traffic lights information. And then, by analyzing the relationship between the error of the sensors and the optimal size of ROI, the adaptively dynamic adjustment (ADA) model was built. Furthermore, according to the multi-sensor data fusion positioning and ADA model, the optimal ROI can be obtained to predict the location of traffic lights. Finally, YOLOv4 is employed to extract and identify the image features. We evaluated our algorithm using a public data set and actual city road test at night. The experimental results demonstrate that the proposed algorithm has a relatively high accuracy rate in complex scenarios and can promote the engineering application of autonomous driving technology.

Operator Use of Multi-Sensor Data Fusion for Airborne Picture Compilation

2020 ◽  
Author(s):  
Geoffrey Ho ◽  
Erin Kim ◽  
Shahzaib Khattak ◽  
Stephanie Penta ◽  
Tharmarasa Ratnasingham ◽  
...  
Keyword(s):  
Data Fusion ◽  
Sensor Data ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion

Multi-sensor Data Fusion Algorithm Based on Adaptive Trust Estimation and Neural Network

2020 ◽  
Author(s):  
Xuexin Zhao ◽  
Junhua Wu ◽  
Maoli Wang ◽  
Guangshun Li ◽  
Haili Yu ◽  
...  
Keyword(s):  
Neural Network ◽  
Data Fusion ◽  
Sensor Data ◽  
Fusion Algorithm ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion

Effective multi-sensor data fusion for chatter detection in milling process

2021 ◽  
Author(s):  
Minh-Quang Tran ◽  
Meng-Kun Liu ◽  
Mahmoud Elsisi
Keyword(s):  
Data Fusion ◽  
Milling Process ◽  
Sensor Data ◽  
Chatter Detection ◽  
Sensor Data Fusion ◽  
Multi Sensor Data Fusion
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