Journal
IEEE ACCESS
Volume 10, Issue -, Pages 18998-19012Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2021.3138051
Keywords
Radar; Sensors; Radar detection; Radar signal processing; Mechanical sensors; Chirp; Support vector machines; Artificial intelligence (AI); autonomous vehicles; machine learning; mm-wave radar
Categories
Funding
- Ontario Centers of Excellence (OCE) Autonomous Vehicle Innovation Network
- Nidec Mobility Inc.
- National Sciences and Engineering Research Council of Canada
- OCE
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We propose a novel algorithm using a frequency modulated continuous wave radar to identify occupied seats in a motor vehicle. By integrating machine learning algorithms with a low-cost radar system, we can predict the number of occupants and their positions. Experimental results show that our proposed system using an SVM classifier achieves an overall accuracy of 97% in both multiclass classification and binary classification methods.
We propose a novel algorithm to identify occupied seats in a motor vehicle, i.e., the number of occupants and their positions, using a frequency modulated continuous wave radar. Instead of using a high-resolution radar, which increases the cost and device size, and performing complex signal processing with several variables to be tuned for each scenario, we integrate machine learning algorithms with a low-cost radar system. Based on heat maps obtained from the Capon beamformer, we train a machine classifier to predict the number of occupants and their positions in a vehicle. We follow two different classification methods: multiclass classification and binary classification. We compare three classifiers: support vector machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF), in terms of accuracy and computational complexity for both testing and training sets. Our proposed system using an SVM classifier achieved an overall accuracy of 97% in classifying the defined scenarios in both multiclass classification and binary classification methods. In addition, to show the effectiveness of our proposed in-vehicle occupancy detection method, we provide the results of a commonly available people counting and tracking method for occupancy detection. Compared to common methods, the effectiveness, robustness, and accuracy of our proposed in-vehicle occupancy detection method are demonstrated.
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