Selected work

Representative Papers

Evaluation of an Uncertainty-Aware Late Fusion Algorithm for Multi-Source Bird's Eye View Detections Under Controlled Noise

Jul 04, 2025

Evaluating multi-sensor bird’s-eye view (BEV) detection fusion performance independently of detector errors remains challenging. To address this, we propose a noise-controllable, systematic evaluation framework and design UniKF—a Kalman filter-based, uncertainty-aware unified post-fusion algorithm. Our framework decouples detector errors from fusion performance via controlled noise injection into BEV feature maps or detections. UniKF explicitly models sensor-specific uncertainties (e.g., calibration and measurement noise) and temporal synchronization offsets, enabling robust cross-sensor fusion under realistic conditions. Experiments across varying noise levels demonstrate that UniKF reduces localization and orientation errors by approximately 3× and size estimation error by 2×, while maintaining near-perfect detection accuracy (99.5%–100% precision and recall). These results significantly outperform state-of-the-art fusion methods, validating both the efficacy of our evaluation framework and the robustness of UniKF under uncertainty.

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A Late Collaborative Perception Framework for 3D Multi-Object and Multi-Source Association and Fusion

Jul 03, 2025

To address the practical bottlenecks in autonomous driving cooperative perception—namely, high communication bandwidth requirements and the need to share model architectures and parameters (compromising privacy)—this paper proposes a lightweight late-fusion framework that relies solely on shared 3D bounding boxes (i.e., class, position, size, and orientation). To our knowledge, this is the first method enabling accurate cross-heterogeneous-system 3D object fusion without accessing agents’ detection model structures or weights. We design a multi-object association and optimization-based fusion algorithm grounded in geometric and semantic consistency, achieving robust matching and error correction at the fusion layer. Experiments demonstrate substantial improvements: position, scale, and orientation estimation errors are reduced to 1/5, 1/7.5, and 1/2 of baseline levels, respectively. Moreover, the method achieves 100% precision and recall in heterogeneous system fusion, while ensuring computational efficiency, strong generalizability, and strict model privacy preservation.

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Recent publications

Latest Papers

Evaluation of an Uncertainty-Aware Late Fusion Algorithm for Multi-Source Bird's Eye View Detections Under Controlled Noise

Jul 04, 2025

Evaluating multi-sensor bird’s-eye view (BEV) detection fusion performance independently of detector errors remains challenging. To address this, we propose a noise-controllable, systematic evaluation framework and design UniKF—a Kalman filter-based, uncertainty-aware unified post-fusion algorithm. Our framework decouples detector errors from fusion performance via controlled noise injection into BEV feature maps or detections. UniKF explicitly models sensor-specific uncertainties (e.g., calibration and measurement noise) and temporal synchronization offsets, enabling robust cross-sensor fusion under realistic conditions. Experiments across varying noise levels demonstrate that UniKF reduces localization and orientation errors by approximately 3× and size estimation error by 2×, while maintaining near-perfect detection accuracy (99.5%–100% precision and recall). These results significantly outperform state-of-the-art fusion methods, validating both the efficacy of our evaluation framework and the robustness of UniKF under uncertainty.

0 citationsRead paper

A Late Collaborative Perception Framework for 3D Multi-Object and Multi-Source Association and Fusion

Jul 03, 2025

To address the practical bottlenecks in autonomous driving cooperative perception—namely, high communication bandwidth requirements and the need to share model architectures and parameters (compromising privacy)—this paper proposes a lightweight late-fusion framework that relies solely on shared 3D bounding boxes (i.e., class, position, size, and orientation). To our knowledge, this is the first method enabling accurate cross-heterogeneous-system 3D object fusion without accessing agents’ detection model structures or weights. We design a multi-object association and optimization-based fusion algorithm grounded in geometric and semantic consistency, achieving robust matching and error correction at the fusion layer. Experiments demonstrate substantial improvements: position, scale, and orientation estimation errors are reduced to 1/5, 1/7.5, and 1/2 of baseline levels, respectively. Moreover, the method achieves 100% precision and recall in heterogeneous system fusion, while ensuring computational efficiency, strong generalizability, and strict model privacy preservation.

0 citationsRead paper