車両軌道の不確かさを学習する適応カルマンフィルタ
Learning Vehicle Trajectory Uncertainty
再帰型ニューラルネットワークで車両の幾何・運動特徴を学習し、カルマンフィルタのプロセスノイズ共分散をリアルタイムに推定する手法を提案した論文。
著者: Barak Or, Itzik Klein
分類: cs.RO, eess.SP
原文アブストラクト
A novel approach for vehicle tracking using a hybrid adaptive Kalman filter is proposed. The filter utilizes recurrent neural networks to learn the vehicle's geometrical and kinematic features, which are then used in a supervised learning model to determine the actual process noise covariance in the Kalman framework. This approach addresses the limitations of traditional linear Kalman filters, which can suffer from degraded performance due to uncertainty in the vehicle kinematic trajectory modeling. Our method is evaluated and compared to other adaptive filters using the Oxford RobotCar dataset, and has shown to be effective in accurately determining the process noise covariance in real-time scenarios. Overall, this approach can be implemented in other estimation problems to improve performance.
