Publications
1.
Song, R.; Hegde, A.; Senel, N.; Festag, A.
Edge-Aided Sensor Data Sharing in Vehicular Communication Networks Proceedings Article
In: IEEE Vehicular Technology Conference (VTC-Spring), pp. 8, Helsinki, Finland, 2022.
Abstract | Links | BibTeX | Tags: Cooperative ITS, cooperative perception, Intelligent Transport Systems, sensor data sharing, vehicular communication
@inproceedings{Song:VTC-Spring:2022,
title = {Edge-Aided Sensor Data Sharing in Vehicular Communication Networks},
author = {R. Song and A. Hegde and N. Senel and A. Festag},
url = {https://ieeexplore.ieee.org/document/9860849},
doi = {10.1109/VTC2022-Spring54318.2022.9860849},
year = {2022},
date = {2022-03-22},
urldate = {2022-03-22},
booktitle = {IEEE Vehicular Technology Conference (VTC-Spring)},
pages = {8},
address = {Helsinki, Finland},
abstract = {Sensor data sharing in vehicular networks can significantly improve the range and accuracy of environmental perception for connected automated vehicles. Different concepts and schemes for dissemination and fusion of sensor data have been developed. It is common to these schemes that measurement errors of the sensors impair the perception quality and can result in road traffic accidents. Specifically, when the measurement error from the sensors – also referred as measurement noise – is unknown and time varying, the performance of the data fusion process is restricted, which represents a major challenge in the calibration of sensors. In this paper, we consider sensor data sharing and fusion in a vehicular network with both, vehicle-to-infrastructure and vehicle-to-vehicle communication. We propose a method, named Bidirectional Feedback Noise Estimation (BiFNoE), in which an edge server collects and caches sensor measurement data from vehicles. The edge estimates the noise and the targets alternately in double dynamic sliding time windows and enhances the distributed cooperative environment sensing at each vehicle with low communication costs. We evaluate the proposed algorithm and data dissemination strategy in an application scenario by simulation and show that the perception accuracy is on average improved by around 80 percent with only 12 kbps uplink and 28 kbps downlink bandwidth.},
keywords = {Cooperative ITS, cooperative perception, Intelligent Transport Systems, sensor data sharing, vehicular communication},
pubstate = {published},
tppubtype = {inproceedings}
}
Sensor data sharing in vehicular networks can significantly improve the range and accuracy of environmental perception for connected automated vehicles. Different concepts and schemes for dissemination and fusion of sensor data have been developed. It is common to these schemes that measurement errors of the sensors impair the perception quality and can result in road traffic accidents. Specifically, when the measurement error from the sensors – also referred as measurement noise – is unknown and time varying, the performance of the data fusion process is restricted, which represents a major challenge in the calibration of sensors. In this paper, we consider sensor data sharing and fusion in a vehicular network with both, vehicle-to-infrastructure and vehicle-to-vehicle communication. We propose a method, named Bidirectional Feedback Noise Estimation (BiFNoE), in which an edge server collects and caches sensor measurement data from vehicles. The edge estimates the noise and the targets alternately in double dynamic sliding time windows and enhances the distributed cooperative environment sensing at each vehicle with low communication costs. We evaluate the proposed algorithm and data dissemination strategy in an application scenario by simulation and show that the perception accuracy is on average improved by around 80 percent with only 12 kbps uplink and 28 kbps downlink bandwidth.