8 citations · 9 across the 7 of their papers we have counts for
7 papers
Multi-Modal Sensor Fusion using Hybrid Attention for Autonomous Driving
Mayank Mayank, Bharanidhar Duraisamy, Florian Geiß +1
Accurate 3D object detection for autonomous driving requires complementary sensors. Cameras provide dense semantics but unreliable depth, while millimeter-wave radar offers precise…
LEO: Graph Attention Network based Hybrid Multi Sensor Extended Object Fusion and Tracking for Autonomous Driving Applications
Mayank Mayank, Bharanidhar Duraisamy, Florian Geiss
Accurate shape and trajectory estimation of dynamic objects is essential for reliable automated driving. Classical Bayesian extended-object models offer theoretical robustness and…
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…