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Radar sensor based machine learning approach for precise vehicle position estimation
Muhammad Sohail1, Abd Ullah Khan2, Moid Sandhu3
1Riphah College of Computing, Riphah International University Faisalabad, Faisalabad, Pakistan.
Scientific Reports
|August 24, 2023
Summary
This study introduces a novel radar-based method for precise vehicle positioning in Vehicular Adhoc Networks (VANETs). The technique improves accuracy by using dynamic radar data and outperforms existing methods in real-time traffic scenarios.
Area of Science:
- * Vehicular Adhoc Networks (VANETs)
- * Sensor Fusion
- * Radar Signal Processing
Background:
- * Conventional Global Positioning System (GPS) and Global Navigation Satellite System (GNSS) methods suffer from data delays and errors, limiting precise vehicle positioning in dynamic environments.
- * Existing radar-based methods use static range and azimuth, proving inefficient for highly dynamic traffic conditions.
- * Accurate relative vehicle positioning is crucial for safe, autonomous, and reliable operation of connected vehicles.
Purpose of the Study:
- * To propose a radar-based relative vehicle positioning estimation method for enhanced precision in VANETs.
- * To leverage dynamic range and azimuth from Frequency Modulated Continuous Wave (FMCW) radar, considering the reference vehicle's speed.
- * To improve the safety and reliability of autonomous driving systems through accurate real-time positioning.
Main Methods:
- * Developed a radar-based relative positioning technique utilizing dynamic range and azimuth from an FMCW radar sensor.
- * Incorporated the reference vehicle's speed to dynamically adjust range and azimuth measurements.
- * Employed the You Only Look Once (YOLO) version 4 algorithm to calculate precision and Intersection over Union (IOU) from radar data.
- * Focused on nearby vehicles with a high probability of interaction for relative distance and speed estimation.
Main Results:
- * Achieved 80.0% precision in vehicle position estimation.
- * Obtained an Intersection over Union (IOU) value up to 87.14%.
- * Demonstrated superior performance compared to state-of-the-art methods under various real-time traffic scenarios.
Conclusions:
- * The proposed radar-based method significantly enhances the precision of relative vehicle positioning in dynamic environments.
- * Dynamic utilization of radar parameters and YOLOv4 integration offers a robust solution for autonomous vehicle navigation.
- * This approach provides a reliable alternative to conventional positioning systems for VANET applications.
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