IRO Journals
Journal of Trends in Computer Science and Smart Technology

UGBCF-Net: Uncertainty-Guided BEV Cross-Attention Fusion Network for Robust Radar-Camera Object Detection

Open Access
Published: 14 July, 2026
Pages: 603-626
  • Bhaskar S V. , Bhaskar S V.
    Department of Computer Science and Engineering, New Horizon College of Engineering, Bengaluru, Karnataka, India
    Department of Computer Science and Engineering, New Horizon College of Engineering, Bengaluru, Karnataka, India
  • Roja Reddy B. Roja Reddy B.
    Department of Electronics and Telecommunication Engineering, RV College of Engineering, Bengaluru, Karnataka, India
    Department of Electronics and Telecommunication Engineering, RV College of Engineering, Bengaluru, Karnataka, India
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How to Cite

S V., Bhaskar, and Roja Reddy B. 2026. “UGBCF-Net: Uncertainty-Guided BEV Cross-Attention Fusion Network for Robust Radar-Camera Object Detection”. Journal of Trends in Computer Science and Smart Technology 8 (3): 603-26. https://doi.org/10.36548/jtcsst.2026.3.009.
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Keywords

Autonomous Driving
3D Object Detection
Radar–Camera Fusion
Bird's-Eye-View (BEV)
Uncertainty Estimation
Cross-Attention Networks
Millimeter-Wave Radar
Deep Learning

Abstract

Object detection under adverse weather remains a major challenge for autonomous driving systems. Cameras do not work well during rain, fog, and darkness, while mmWave radar works accurately in all of these conditions, regardless of illumination and precipitation. In this paper, we propose UGBCF-Net – the Uncertainty-Guided Bird's-Eye-View Cross-Attention Fusion Network, which incorporates radar confidence estimation based on physical laws and adaptive multimodal fusion. Our proposed architecture estimates radar confidence from radar cross-section measurements, maintains the Doppler channel by means of four-channel BEV representation, and uses uncertainty-guided weighting with a lightweight pooled cross-attention network for efficient feature fusion. Experimental results on the nuScenes v1.0-trainval dataset have shown that our UGBCF-Net reaches 0.683 mAP with 8.9 M parameters, operating at 56 FPS on a single NVIDIA RTX 3050 GPU. UGBCF-Net has achieved an accuracy similar to large-fusion networks and showed great robustness, reaching night recall equal to 0.884 with recall rates of 93.1%, 95.9% and 97.4% for vehicles, pedestrians, and cyclists, respectively.

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