  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 % with only 12 kbps uplink and 28
kbps downlink bandwidth.
