Contact devices for human target detection mainly include pressure sensors and wearable devices such as bracelets. For instance, in April 2017, Tao et al. proposed a graphene paper pressure sensor that can be used for respiratory and pulse detection as well as motion detection [
5]. However, the main challenge for pressure sensors in a vehicle testing environment is that they are unable to distinguish between heavy objects and human bodies, leading to false judgments. To address this, Márquez et al. combined a smart multisensory bracelet to develop a control platform capable of recognizing human activity and detecting situations that may result in body injury [
6].
Non-contact devices can be divided into two categories: radar-based and non-radar based. Among non-radar-based systems, Perra et al. proposed a method that combines thermal imaging from an infrared sensor array with a pattern recognition algorithm to detect the presence of people in indoor environments [
7]. The algorithm can count people in a confined indoor environment, similar to that of a car, with high accuracy by detecting the movement of users with different walking speeds based on different user–sensor distances and the typical temperature ranges of residential environments. However, the algorithm’s performance is degraded by different distances, speeds, and thermal noise. Additionally, devices based on infrared systems have inherent limitations due to the pyroelectric effect and cannot recognize stationary targets. Another widely used non-contact system is based on camera images for occupant detection, as presented by Papakis et al. [
8]. This system constructs a deep learning neural network to sense body movements and actions and identify the number of people in a vehicle. Alternatively, a thermal imaging camera can also enable the detection of the presence of people indoors, but this method is expensive [
9]. Additionally, an ultrasound-based system can detect static human vital signs, but monitoring accuracy decreases in complex in-vehicle environments [
10].
Various radar-based systems and methodologies have been developed and are available in the literature. One such system is based on ultra-wideband radar [
11], which determines the presence of passengers mainly by analyzing parameters such as signal mainstream and size. This system detects the presence or absence of vital sign signals to detect the presence of people. Signal processing techniques such as the fast Fourier transform (FFT) and variational mode decomposition (VMD) are applied to extract information about vital signs. However, the accuracy of the respiratory cycle estimation is affected by body movements. Lim et al. proposed a method based on pulse radio ultra-wideband radar that feeds the processed signals into a deep neural network [
12]. However, the radar used in this method is too large to be embedded in an automotive environment, and it is less robust and challenging to implement in an embedded device or DSP due to the use of integrated learning and multilayer perception techniques. Lazaro et al. proposed a detection method based on peak amplitude difference standard deviation estimation and threshold comparator [
13]. The respiration rate is measured from the time interval between two peaks associated with breathing movement. Hyun et al. proposed a Doppler spectrum-based vehicle passenger detection scheme [
14]. In their research, they utilized two motion features and one vital sign feature, and then applied machine learning-based identification using a bifurcated decision tree (BDT).
The proposed solution in this work is based on a Frequency Modulated Continuous Wave (FMCW) radar, which is inexpensive, small in size, and easy to deploy. It is equipped with a built-in chip for offline computing and is able to derive data on multiple dimensions of the target, such as distance, angle, speed, and target size due to its signal characteristics. Therefore, a multi-input multi-output FMCW radar is suitable for the in-vehicle scenario. Additionally, Caddemi et al. developed a child recognition system using sawtooth wave radar [
15]. In the case of a child in a child seat, the FMCW radar beam is pointed at the seat, and the target distance is calculated by FFT using the distance difference between the presence and absence of the child. Although this scheme is easy to implement, its primary disadvantage is that the radar can only detect one position, and the seat cannot move back and forth during its use unless the threshold for determining the child’s presence is reset after the seat movement. In 2021, Song et al. used an FFT-based system to identify the child’s presence by analyzing the respiratory band (0.1–0.4 Hz) and the heartbeat band (0.8–1.7 Hz) [
16]. Cardillo et al. [
17] presents a detection system for a non-contact engine room that utilizes a combination of radar signal processing techniques to detect the presence and location of people inside the vehicle. The experimental results confirm the feasibility and effectiveness of the system, but the study does not test it in a real-world scenario where multiple targets are moving, which is a problem that the paper aims to address. Diewald et al. used Feko software to simulate the directional map of the transceiver antenna and then modeled the full vehicle environment [
18]. However, the work concluded that target extraction by phase information is not possible if there are multiple moving targets. Abedi et al. modeled the vehicle and the antenna using a full-wave electromagnetic simulation to find the best installation position of the AWR1443 type of radar [
19]. Song et al., on the other hand, used power and Wiener entropy at different distances for the estimation of different seat occupancy situations [
20]. Another class of methods relies on the generation of the target point cloud map using the multidimensional information of FMCW. Zhang et al. proposed a more accurate point cloud generation method by combining the moving target indication (MTI) algorithm and range-Doppler imaging (RDI) using both dynamic and static characteristics [
21]. Abedi et al. fed the heat map of AoA’s output into different classifiers, including support vector machine (SVM), k-nearest neighbors (KNN), and random forest (RF), to classify passenger occupancy [
22]. As mentioned by Abedi [
22] in his paper, the group tracking algorithm based on point cloud data may not produce accurate recognition results when passengers are moving. Therefore, he employed machine learning techniques to improve the algorithm’s performance. The objective of this paper is to address the challenging scenario of monitoring multiple passengers with movements inside a vehicle. Additionally, the proposed algorithm in this paper is characterized by low complexity and can be easily implemented on embedded systems. In general, there are two main approaches for target detection using FMCW, including passenger presence detection based on their breathing and heartbeat characteristics [
14,
15,
16,
17,
18,
19,
20] and output of angle of arrival (AoA) [
21,
22]. However, the former method has limitations due to the impact of the moving vehicle and passengers’ movements, which can significantly affect the measurement of vital signs. Similarly, the FMCW-based method of extracting micro doppler features is unsatisfactory in situations where multiple passengers are moving simultaneously, which is the most realistic scenario considered in this paper. Therefore, this paper proposes a point cloud generation method that is less affected by movement and interference. The focus of this paper is not the generation of point clouds, but the post-processing process for low precision point clouds. The purpose of this paper is to propose a detection scheme that can adapt to different sitting positions and movements of passengers based on the low precision point cloud method, to achieve better robustness and improve detection accuracy under the condition of low complexity. The proposed method uses clustering and a state machine to achieve robust presence detection with low computational complexity and ease of deployment on the radar’s microcontroller unit (MCU). This method offers better robustness to passengers’ actions, and it is characterized by low cost and low latency. Unlike other methods in
Table 1, the proposed method has been experimentally demonstrated to be applicable in scenarios that closely resemble a real car environment with moving passengers.