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MVDFusion: Multimodal Vehicle Detection in Foggy Weather Using LiDAR and Radar Fusion.

Jiake Tian¹, Yan Gao¹, Xin Xia^2,3,4,5

¹Pengcheng Laboratory, Shenzhen 518055, China.

Sensors (Basel, Switzerland)

|May 13, 2026

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MVDFusion enhances vehicle detection in fog by combining LiDAR and millimeter-wave (mmWave) radar data. This multi-modal approach overcomes radar

Area of Science:

Autonomous Driving Systems
Sensor Fusion for Perception
Robotics and Intelligent Systems

Background:

Millimeter-wave (mmWave) radar offers robust vehicle detection in adverse weather but suffers from data sparsity and limited height information.
Existing methods struggle with the inherent limitations of mmWave radar, hindering its widespread application in complex environments.

Purpose of the Study:

To develop a novel multi-modal vehicle detection framework (MVDFusion) integrating LiDAR and mmWave radar data.
To address the challenges of sparse radar data and insufficient height estimation for improved perception in foggy conditions.

Main Methods:

Proposed MVDFusion framework leverages LiDAR data to compensate for mmWave radar limitations.
Introduced a radar height query module for enhanced height estimation.

Keywords:

autonomous driving foggy weather millimeter-wave radar sensor fusion vehicle detection

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Developed a radar-LiDAR query fusion module for improved feature representation and deep feature-level integration.

Main Results:

MVDFusion demonstrated superior performance and robustness in foggy environments on the Oxford Radar RobotCar dataset.
Achieved state-of-the-art detection accuracies: 95.8% (IoU 0.5), 94.2% (IoU 0.65), and 81.5% (IoU 0.8).

Conclusions:

The proposed MVDFusion framework effectively fuses mmWave radar and LiDAR data for robust vehicle detection.
This multi-modal approach significantly enhances perception capabilities, particularly in challenging foggy conditions.