# Learning Vehicle Trajectory Uncertainty

arXiv: https://arxiv.org/abs/2206.04409 · HTML: https://mlsanity.com/paper/2206.04409/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2022.

## Built on by (most cited first, 8 of 8)

- [A Hybrid Model and Learning-Based Adaptive Navigation Filter](https://mlsanity.com/paper/2207.12082/) (2022)
- [Adaptive Kalman-Informed Transformer](https://mlsanity.com/paper/2401.09987/) (2024)
- [Data-Driven Meets Navigation: Concepts, Models, and Experimental Validation](https://mlsanity.com/paper/2210.02930/) (2022)
- [Deep Learning for Inertial Sensor Alignment](https://mlsanity.com/paper/2212.11120/) (2022)
- [A Hybrid Adaptive Velocity Aided Navigation Filter with Application to INS/DVL Fusion](https://mlsanity.com/paper/2211.01329/) (2022)
- [Learning Position From Vehicle Vibration Using an Inertial Measurement Unit](https://mlsanity.com/paper/2303.03942/) (2023)
- [Learning-Based Speed Estimation from Accelerometer-Only Inertial Sensing](https://mlsanity.com/paper/2401.07468/) (2024)
- [Selection-Induced Contraction of Innovation Statistics in Gated Kalman Filters](https://mlsanity.com/paper/2512.18508/) (2025)

## Abstract (arXiv)

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.