Learning Vehicle Trajectory Uncertainty

The linear Kalman filter is commonly used for vehicle tracking. This filter requires knowledge of the vehicle trajectory and the statistics of the system and measurement models. In real-life scenarios, prior assumptions made while determining those models do not hold. As a consequence, the overall filter performance degrades and in some situations the estimated states diverge. To overcome the uncertainty in the {vehicle kinematic} trajectory modeling, additional artificial process noise may be added to the model or different types of adaptive filters may be employed. This paper proposes {a hybrid} adaptive Kalman filter based on {model and} machine learning algorithms. First, recurrent neural networks are employed to learn the vehicle's geometrical and kinematic features. In turn, those features are plugged into a supervised learning model, thereby providing the actual process noise covariance to be used in the Kalman framework. The proposed approach is evaluated and compared to six other adaptive filters using the Oxford RobotCar dataset. The proposed framework can be implemented in other estimation problems to accurately determine the process noise covariance in real-time scenarios.