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.

翻译：提出了一种基于混合自适应卡尔曼滤波的车辆跟踪新方法。该滤波器利用循环神经网络学习车辆的几何与运动学特征，随后通过监督学习模型确定卡尔曼框架中的实际过程噪声协方差。该方法解决了传统线性卡尔曼滤波器因车辆运动学轨迹建模中的不确定性而导致性能下降的问题。我们使用牛津RobotCar数据集对所提方法进行了评估，并与其他自适应滤波器进行了对比，结果表明该方法能够在实时场景中有效且准确地确定过程噪声协方差。总体而言，该方案可应用于其他估计问题中以提升性能。