Neural Network Based Uncertainty Prediction for Autonomous Vehicle Application - Zhang, Feihu; Martinez, Clara Marina; Clarke, Daniel; Cao, Dongpu; Knoll, Alois (Frontiers in Neurorobotics, 2019)
This paper proposes a framework for uncertainty prediction in complex fusion networks, where signals become available sporadically. Assuming no information of sensor characteristics is available, a surrogated model of uncertainty is yielded directly from data through artificial neural networks. The strategy developed is applied to autonomous vehicle localization through odometry sensors (speed and orientation), so as to determine the location uncertainty in the trajectory. The results obtained allow for fusion of autonomous vehicle location measurements, and effective correction of the accumulated odometry error in most scenarios. The neural networks applicability and generalization capacity are proven, evidencing the suitability of the presented methodology for uncertainty estimation in non-linear and intractable processes.