CAT-GAN-UKF: category-aware online adaptive unscented kalman filtering for trajectory-level multi-object state...

Area of Science:

• Robotics and Autonomous Systems
• State Estimation
• Machine Learning for Perception

Background:

• Traditional Unscented Kalman Filters (UKFs) use fixed uncertainty parameters, limiting performance in dynamic multi-object sensing environments.
• Object categories and sensing conditions significantly influence noise characteristics, yet standard UKFs do not adapt.
• Existing methods often lack robust online adaptation for varying environmental factors.

Purpose of the Study:

• Introduce a novel adaptive filtering framework, Category-Aware Adaptive Unscented Kalman Filtering (CAT-GAN-UKF), for trajectory-level state estimation.
• Enable dynamic, context-dependent adjustment of UKF uncertainty parameters using a category-conditioned learning module.
• Evaluate the performance improvements of CAT-GAN-UKF against various baselines in heterogeneous autonomous driving scenarios.

Main Methods:

• Developed CAT-GAN-UKF, a framework that updates uncertainty parameters online via a category-conditioned module using innovation-based diagnostics and contextual cues.
• Preserved the recursive Bayesian structure of the UKF for online adaptation.
• Employed a leakage-free experimental protocol on a large-scale autonomous driving dataset, training on past scenes and evaluating on future scenes for generalized adaptation.

Main Results:

• CAT-GAN-UKF demonstrated consistent improvements in trajectory-level estimation accuracy across diverse object categories and sensing regimes.
• Achieved improved consistency between predicted uncertainty and realized estimation error compared to static and adaptive baselines.
• Validated gains attributed to learned context-dependent adaptation, not just parameter tuning.

Conclusions:

• Category-conditioned online adaptation significantly enhances estimator-level performance in heterogeneous multi-object sensing environments.
• CAT-GAN-UKF offers a robust method for improving state estimation accuracy by dynamically adapting uncertainty.
• Future work can focus on integrating this adaptive estimator into full end-to-end multi-object tracking pipelines.