No abstract

In this paper, we use a low-cost low-power mm-wave frequency modulated continuous wave (FMCW) radar for the in-vehicle occupant detection. We propose an algorithm using Capon filter for the joint range-azimuth estimation. Then, the minimum necessary features are extracted to train machine learning classifiers to have reasonable computational complexity while achieving high accuracy. In addition, experiments were carried out in a minivan to detect occupancy of each row using support vector machine (SVM). Finally, our proposed system achieved 97.8% accuracy on average in finding the defined scenarios. Moreover, The system can correctly identify if the vehicle is occupied or not with 100% accuracy.

According to the group Kids and Cars, since 1990, nearly 1000 kids lost their lives because they were deliberately or unintentionally left in parked vehicles to potentially overheat or freeze. The development of technology able to prevent and address this serious, worldwide problem is crucial. In this paper, we deploy a radar-based sensor for in-vehicle presence-absence detection of a living body. We present a novel radar signal processing technique to identify the presence or absence of a living body in a vehicle using a mm-wave frequency-modulated continuous-wave (FMCW) radar. Our proposed method is based on reflections from breathing cycles creating correlated and consistent micro-Doppler effects over time. The performance of the system is evaluated with adults and two phantoms mimicking the breathing of children in various scenarios. The results show that we can clearly detect any tiny living body in vehicles with 100% accuracy without a need for any compute-intensive complex signal processing, making the system of extreme low-cost. The results demonstrate the high sensitivity and robustness of the mm-wave system in extensive studies over the course of multiple months.

No abstract

We propose a novel algorithm to identify occupied seats, 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 area, 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 finding 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 common group tracking and people counting method of occupancy detection. Compared to the common method, the effectiveness, robustness, and accuracy of our proposed in-vehicle occupancy detection method are shown.