  A major challenge in cooperative sensing is to weight the measurements taken
from the various sources to get an accurate result. Ideally, the weights should
be inversely proportional to the error in the sensing information. However,
previous cooperative sensor fusion approaches for autonomous vehicles use a
fixed error model, in which the covariance of a sensor and its recognizer
pipeline is just the mean of the measured covariance for all sensing scenarios.
The approach proposed in this paper estimates error using key predictor terms
that have high correlation with sensing and localization accuracy for accurate
covariance estimation of each sensor observation. We adopt a tiered fusion
model consisting of local and global sensor fusion steps. At the local fusion
level, we add in a covariance generation stage using the error model for each
sensor and the measured distance to generate the expected covariance matrix for
each observation. At the global sensor fusion stage we add an additional stage
to generate the localization covariance matrix from the key predictor term
velocity and combines that with the covariance generated from the local fusion
for accurate cooperative sensing. To showcase our method, we built a set of
1/10 scale model autonomous vehicles with scale accurate sensing capabilities
and classified the error characteristics against a motion capture system.
Results show an average and max improvement in RMSE when detecting vehicle
positions of 1.42x and 1.78x respectively in a four-vehicle cooperative fusion
scenario when using our error model versus a typical fixed error model.
