8 citations · 9 across the 5 of their papers we have counts for
5 papers
Adaptive Learned State Estimation based on KalmanNet
Arian Mehrfard, Bharanidhar Duraisamy, Stefan Haag +2
Hybrid state estimators that combine model-based Kalman filtering with learned components have shown promise on simulated data, yet their performance on real-world automotive data…
Offline Auto Labeling: BAAS
Stefan Haag, Bharanidhar Duraisamy, Felix Govaers +3
This paper introduces BAAS, a new Extended Object Tracking (EOT) and fusion-based label annotation framework for radar detections in autonomous driving. Our framework utilizes Baye…
Performance Evaluation of Deep Learning-Based State Estimation: A Comparative Study of KalmanNet
Arian Mehrfard, Bharanidhar Duraisamy, Stefan Haag +1
Kalman Filters (KF) are fundamental to real-time state estimation applications, including radar-based tracking systems used in modern driver assistance and safety technologies. In…
UNIFY: Multi-Belief Bayesian Grid Framework based on Automotive Radar
Stefan Haag, Bharanidhar Duraisamy, Daniel Pfrommer +3
Grid maps are widely established for the representation of static objects in robotics and automotive applications. Though, incorporating velocity information is still widely examin…
Automated Ground Truth Estimation For Automotive Radar Tracking Applications With Portable GNSS And IMU Devices
Nicolas Scheiner, Stefan Haag, Nils Appenrodt +4
Baseline generation for tracking applications is a difficult task when working with real world radar data. Data sparsity usually only allows an indirect way of estimating the origi…