Bulk Sensor Data Sharing Using Millimeter Wave V2X for Enhanced Safety and Comfort in Mobility

2018 ◽  
Author(s):  
Masataka Irie ◽  
Gaius Wee Yao Huang ◽  
Michael Sim Hong Cheng ◽  
Kazuaki Takahashi
Keyword(s):  
Data Sharing ◽  
Millimeter Wave ◽  
Sensor Data

scholarly journals Simultaneous Target Classification and Moving Direction Estimation in Millimeter-Wave Radar System

Sensors ◽  
2021 ◽  
Vol 21 (15) ◽  
pp. 5228
Author(s):  
Jin-Cheol Kim ◽  
Hwi-Gu Jeong ◽  
Seongwook Lee
Keyword(s):  
Millimeter Wave ◽  
Radar System ◽  
Sensor Data ◽  
Test Field ◽  
Target Classification ◽  
Radar Sensor ◽  
Millimeter Wave Radar ◽  
Direction Estimation ◽  
Wave Radar ◽  
Moving Direction

In this study, we propose a method to identify the type of target and simultaneously determine its moving direction in a millimeter-wave radar system. First, using a frequency-modulated continuous wave (FMCW) radar sensor with the center frequency of 62 GHz, radar sensor data for a pedestrian, a cyclist, and a car are obtained in the test field. Then, a You Only Look Once (YOLO)-based network is trained with the sensor data to perform simultaneous target classification and moving direction estimation. To generate input data suitable for the deep learning-based classifier, a method of converting the radar detection result into an image form is also proposed. With the proposed method, we can identify the type of each target and its direction of movement with an accuracy of over 95%. Moreover, the pre-trained classifier shows an identification accuracy of 85% even for newly acquired data that have not been used for training.

Pastry Based Sensor Data Sharing

2009 ◽  
Author(s):  
Wang-Cheol Song ◽  
Sung-Su Kim ◽  
Seung-Joon Seok ◽  
Deokjai Choi
Keyword(s):  
Data Sharing ◽  
Sensor Data

scholarly journals Attitudes towards Participation in a Passive Data Collection Experiment

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6085
Author(s):  
Bence Ságvári ◽  
Attila Gulyás ◽  
Júlia Koltai
Keyword(s):  
Data Collection ◽  
Data Sharing ◽  
Online Survey ◽  
User Participation ◽  
Sensor Data ◽  
Smart Devices ◽  
Future Research ◽  
General Level ◽  
Willingness To Participate ◽  
Passive Data

In this paper, we present the results of an exploratory study conducted in Hungary using a factorial design-based online survey to explore the willingness to participate in a future research project based on active and passive data collection via smartphones. Recently, the improvement of smart devices has enabled the collection of behavioural data on a previously unimaginable scale. However, the willingness to share this data is a key issue for the social sciences and often proves to be the biggest obstacle to conducting research. In this paper we use vignettes to test different (hypothetical) study settings that involve sensor data collection but differ in the organizer of the research, the purpose of the study and the type of collected data, the duration of data sharing, the number of incentives and the ability to suspend and review the collection of data. Besides the demographic profile of respondents, we also include behavioural and attitudinal variables to the models. Our results show that the content and context of the data collection significantly changes people’s willingness to participate, however their basic demographic characteristics (apart from age) and general level of trust seem to have no significant effect. This study is a first step in a larger project that involves the development of a complex smartphone-based research tool for hybrid (active and passive) data collection. The results presented in this paper help improve our experimental design to encourage participation by minimizing data sharing concerns and maximizing user participation and motivation.

scholarly journals Automated Driving with Cooperative Perception Using Millimeter-Wave V2V Communications for Safe Overtaking

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2659
Author(s):  
Ryuichi Fukatsu ◽  
Kei Sakaguchi
Keyword(s):  
Millimeter Wave ◽  
Data Rate ◽  
Sensor Data ◽  
Automated Driving ◽  
V2v Communication ◽  
Additional Information ◽  
Safety And Reliability ◽  
V2v Communications ◽  
Vehicle Movement ◽  
Blind Spots

The combination of onboard sensors on vehicles with wireless communication has great advantages over the conventional driving systems in terms of safety and reliability. This technique is often called cooperative perception. Cooperative perception is expected to compensate for blind spots in dynamic maps, which are caused by obstacles. Few blind spots in dynamic maps can improve the safety and reliability of driving thanks to the additional information beyond the sensing of the onboard sensors. In this paper, we analyzed the required sensor data rate to be exchanged for the cooperative perception in order to enable a new level of safe and reliable automated driving in overtaking scenario. The required sensor data rate was calculated by the combination of recognition and vehicle movement to adopt realistic assumptions. In the end of the paper, we compared the required sensor data rate with the outage data rate realized by the conventional V2V communication and millimeter-wave communication. The results showed the indispensability of millimeter-wave communications in automated driving systems.

scholarly journals Automated Driving with Cooperative Perception Based on CVFH and Millimeter-Wave V2I Communications for Safe and Efficient Passing through Intersections

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5854
Author(s):  
Ryuichi Fukatsu ◽  
Kei Sakaguchi
Keyword(s):  
Wireless Communications ◽  
Millimeter Wave ◽  
Traffic Congestion ◽  
Data Rate ◽  
Sensor Data ◽  
Connected Vehicles ◽  
Automated Driving ◽  
Connected Cars ◽  
Millimeter Wave Communications ◽  
Blind Spots

The development of automated driving is actively progressing, and connected cars are also under development. Connected cars are the technology of connecting vehicles to networks so that connected vehicles can enhance their services. Safety services are among the main services expected in connected car society. Cooperative perception belongs to safety services and improves safety by visualizing blind spots. This visualization is achieved by sharing sensor data via wireless communications. Therefore, the number of visualized blind spots highly depends upon the performance of wireless communications. In this paper, we analyzed the required sensor data rate to be shared for the cooperative perception in order to realize safe and reliable automated driving in an intersection scenario. The required sensor data rate was calculated by the combination of recognition and crossing decisions of an automated driving vehicle to adopt realistic assumptions. In this calculation, CVFH was used to derive tight requirements, and the minimum required braking aims to alleviate the traffic congestion around the intersection. At the end of the paper, we compare the required sensor data rate with the outage data rate realized by conventional and millimeter-wave communications, and show that millimeter-wave communications can support safe crossing at a realistic velocity.

scholarly journals DSSM: Distributed Streaming Data Sharing Manager

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1344
Author(s):  
Hiroaki Fukuda ◽  
Ryota Gunji ◽  
Tadahiro Hasegawa ◽  
Paul Leger ◽  
Ismael Figueroa
Keyword(s):  
Control System ◽  
Data Sharing ◽  
Robot Control ◽  
Streaming Data ◽  
Sensor Data ◽  
Software Systems ◽  
Robot Operating System ◽  
Multiple Sensors ◽  
Advantages And Disadvantages ◽  
Robot Control System

Developing robot control software systems is difficult because of a wide variety of requirements, including hardware systems and sensors, even though robots are demanding nowadays. Middleware systems, such as Robot Operating System (ROS), are being developed and widely used to tackle this difficulty. Streaming data Sharing Manager (SSM) is one of such middleware systems that allow developers to write and read sensor data with timestamps using a Personal Computer (PC). The timestamp feature is essential for the robot control system because it usually uses multiple sensors with their own measurement cycles, meaning that measured sensor values with different timestamps become useless for the robot control. Using SSM allows developers to use measured sensor values with the same timestamps; however, SSM assumes that only one PC is used. Thereby, if one process consumes CPU resources intensively, other processes cannot finish their assumed deadlines, leading to the unexpected behavior of a robot. This paper proposes an SSM middleware, named Distributed Streaming data Sharing Manager (DSSM), that enables distributing processes on SSM to different PCs. We have developed a prototype of DSSM and confirmed its behavior so far. In addition, we apply DSSM to an existing real SSM based robot control system that autonomously controls an unmanned vehicle robot. We then reveal its advantages and disadvantages via several experiments by measuring resource usages.

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