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Forward Collision Warning Strategy Based on Millimeter-Wave Radar and Visual Fusion
Chenxu Sun1, Yongtao Li1, Hanyan Li2
1School of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545616, China.
Sensors (Basel, Switzerland)
|December 9, 2023
Summary
This study introduces an improved forward collision warning (FCW) system using enhanced vision and radar algorithms. The new decision-level fusion strategy significantly reduces false and missed alarms in challenging driving conditions.
Area of Science:
- Automotive Engineering
- Computer Vision
- Sensor Fusion
Background:
- Forward collision warning (FCW) systems are crucial for road safety.
- Existing multi-sensor fusion methods struggle with high false alarm rates in adverse conditions.
Purpose of the Study:
- To develop a novel decision-level fusion collision warning strategy for FCW systems.
- To enhance radar tracking and vision detection algorithms to minimize false and missed alarms.
Main Methods:
- An adaptive Kalman filter with an information entropy-based memory index for radar target tracking.
- An enhanced YOLOv5s model incorporating a Selective Kernel and Bottleneck Attention Mechanism (SKBAM) for improved vehicle detection.
- A decision-level fusion strategy combining millimeter-wave radar and vision data with a minimum safe distance model.
Main Results:
- The proposed algorithm demonstrated a reduction in the false alarm rate by 11.619%.
- The missed alarm rate was reduced by 15.672% compared to traditional methods.
- Effective performance was validated across diverse weather and road conditions.
Conclusions:
- The developed decision-level fusion strategy significantly improves FCW system reliability.
- Enhanced radar and vision algorithms contribute to reduced false and missed alarms.
- The approach offers a more robust solution for forward collision warning in complex environments.
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