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Adaptive Sensor Fusion for Robust Perception in Dense Fog: A Gated Vision and LiDAR Integration Framework.

Fengyuan Zhang¹, Zixuan Guo², Jianbo Ding³

¹Tandon School of Engineering, New York University, New York, NY 10010, USA.

Sensors (Basel, Switzerland)

|June 26, 2026

Summary

This study introduces an adaptive fusion framework using gated imaging and LiDAR for robust autonomous driving perception in dense fog. The novel approach significantly improves obstacle detection accuracy in low visibility conditions.

Keywords:

3D object detection LiDAR adaptive sensor fusion adverse weather autonomous driving dense fog gated imaging uncertainty estimation

Related Experiment Videos

Area of Science:

Computer Vision
Robotics
Sensor Fusion

Background:

Autonomous driving systems struggle with perception failures in dense fog due to atmospheric scattering affecting conventional RGB cameras.
Existing RGB-LiDAR fusion methods degrade significantly in low visibility, impacting safety-critical applications.

Purpose of the Study:

To develop an adaptive multi-modal fusion framework for robust obstacle detection in dense fog conditions.
To enhance the reliability of autonomous driving perception systems operating in adverse weather.

Main Methods:

Integration of gated imaging and 3D LiDAR point clouds using an Adaptive Feature-Weighting Network (AFW-Net).
Cross-modal feature extraction leveraging complementary sensor properties.
Attention-based adaptive fusion with uncertainty estimation for dynamic modality weighting.
Degradation-aware training strategy with weather-specific augmentation.

Main Results:

Achieved over 82% average precision (AP) in dense fog (50 m visibility), a 23.7% improvement over state-of-the-art RGB-LiDAR fusion.
Demonstrated robust obstacle detection maintaining performance in low-visibility scenarios.
Validated the effectiveness of adaptive weighting and generalization capabilities through ablation and cross-dataset studies.

Conclusions:

The proposed adaptive fusion framework significantly enhances autonomous driving perception robustness in low-visibility conditions.
The AFW-Net dynamically balances sensor contributions, outperforming existing fusion techniques.
This work provides a robust perception paradigm for safety-critical autonomous systems in adverse weather.