Learning vehicle trajectory uncertainty

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Article number106101
JournalEngineering Applications of Artificial Intelligence
StatePublished - Jun 2023

Bibliographical note

Publisher Copyright:
© 2023 Elsevier Ltd


  • Adaptive estimation
  • Curvature estimation
  • Kalman filter
  • Long short-term memory
  • Recurrent neural networks
  • Trajectory modeling
  • Vehicle tracking

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering


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