4 papers
Multi-Modal Sensor Fusion using Hybrid Attention for Autonomous Driving
Mayank Mayank, Bharanidhar Duraisamy, Florian Geià +1
Accurate 3D object detection for autonomous driving requires complementary sensors. Cameras provide dense semantics but unreliable depth, while millimeter-wave radar offers precise…
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…
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…
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…