Millimeter-Wave Radar-Based Vehicle In-Cabin Occupancy Detection Using Explainable Machine Learning
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- Sato, Kotone
- Department of Electrical and Computer Engineering, Yokohama National University
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- Wandale, Steven
- School of Natural and Applied Sciences, University of Malawi
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- Ichige, Koichi
- Department of Electrical and Computer Engineering, Yokohama National University
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- Kimura, Kazuya
- Murata Manufacturing Company Ltd.
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- Sugiura, Ryo
- Murata Manufacturing Company Ltd.
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説明
This article proposes a method for vehicle in-cabin occupancy detection using millimeter-wave (mmWave) radar and machine learning (ML) scheme, and also proposes a simplified ML model. The proposed method was designed for child presence detection (CPD) because it is easy to implement in vehicles and can detect infants with small reflections without external factors by implementing many features. The ML models we use are extreme gradient boosting (XGBoost) and deep neural network (DNN), which achieve 100% accuracy by using 74 different features, and expected to perform better than conventional models implementing similar detection methods. However, these models are black boxes in structure and have low explainability. Therefore, our method improves the explainability and simplicity of model of XGBoost. We develop a simple approximate model of XGBoost which improves explainability and simplicity of the method. Our method provides a new evaluation algorithm and model saving method to further simplify and speed up the approximate model. The proposed simple model can easily be implemented on low-cost firmware with radar modules.
収録刊行物
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- IEEE Sensors Journal
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IEEE Sensors Journal 24 (15), 24288-24298, 2024-08-01
Institute of Electrical and Electronics Engineers (IEEE)
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キーワード
- Radar, Radar antennas, Feature extraction, Radar detection, Computational modeling, Millimeter wave communication, Data models
- Explainable Artificial Intelligence, Occupancy Detection, Deep Neural Network, Machine Learning Models, Evaluation Of Algorithms, Extreme Gradient Boosting, XGBoost Model,
- Machine Learning Strategies, Millimeter-wave Radar, Test Data, Typical Features, Support Vector Machine, Decision Tree, Model Size, Comparison Of Differences, Azimuth Angle,
- Multiple-input Multiple-output, Child Feeding, Deep Neural Network Model, Data Frame, Frequency-modulated Continuous-wave Radar, Frequency Modulated Continuous Wave,
- Mechanical Sensors, Direction Of Arrival, Label Prediction, Front Seat, Feature Names, Distance Information, Tree Depth, Detection System
- Extreme gradient boosting (XGBoost), frequency-modulated continuous-wave (FMCW) radar, occupancy detection
詳細情報 詳細情報について
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- CRID
- 1050020519548287872
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- NII書誌ID
- AA11551238
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- HANDLE
- 10131/0002001369
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- ISSN
- 1530437X
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- 本文言語コード
- en
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- 資料種別
- journal article
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- データソース種別
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- IRDB