论文标题
学习车辆轨迹不确定性
Learning Vehicle Trajectory Uncertainty
论文作者
论文摘要
提出了一种使用混合自适应Kalman滤波器进行车辆跟踪的新方法。该过滤器利用经常性的神经网络来学习车辆的几何和运动学特征，然后将其用于监督学习模型中，以确定卡尔曼框架中的实际过程噪声协方差。这种方法解决了传统的线性卡尔曼过滤器的局限性，由于车辆运动运动轨迹建模的不确定性，可能会遭受性能降低的局限性。使用牛津机器人数据集对我们的方法进行了评估并将其与其他自适应过滤器进行比较，并已证明可以在实时场景中准确确定过程噪声协方差。总体而言，可以在其他估计问题中实施这种方法以提高性能。
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.
