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A major challenge in cooperative sensing is to weight the measurements takenfrom the various sources to get an accurate result. Ideally, the weights shouldbe inversely proportional to the error in the sensing information. However,previous cooperative sensor fusion approaches for autonomous vehicles use afixed error model, in which the covariance of a sensor and its recognizerpipeline is just the mean of the measured covariance for all sensing scenarios.The approach proposed in this paper estimates error using key predictor termsthat have high correlation with sensing and localization accuracy for accuratecovariance estimation of each sensor observation. We adopt a tiered fusionmodel consisting of local and global sensor fusion steps. At the local fusionlevel, we add in a covariance generation stage using the error model for eachsensor and the measured distance to generate the expected covariance matrix foreach observation. At the global sensor fusion stage we add an additional stageto generate the localization covariance matrix from the key predictor termvelocity and combines that with the covariance generated from the local fusionfor accurate cooperative sensing. To showcase our method, we built a set of1/10 scale model autonomous vehicles with scale accurate sensing capabilitiesand classified the error characteristics against a motion capture system.Results show an average and max improvement in RMSE when detecting vehiclepositions of 1.42x and 1.78x respectively in a four-vehicle cooperative fusionscenario when using our error model versus a typical fixed error model.

Accurate Cooperative Sensor Fusion by Parameterized Covariance Generation for Sensing and Localization Pipelines in CAVs