# Car-Sense: Vehicle Occupant Legacy Hazard Detection Method Based on DFWS
###### Abstract  
Casualties caused by people trapped in cars have been common in recent years. Despite a variety of solutions, complex detection devices need to be arranged, or privacy is poor. Since device-free Wi-Fi sensing has attracted much attention due to its simplicity, low cost, and no need for additional hardware, this paper proposes a contactless wireless Wi-Fi sensing-based method for detecting people left in cars: Car-Sense. The method uses ESP32 devices in the vehicle to build a wireless Wi-Fi network for low-cost, real-time, and accurate personnel awareness. By capturing and analyzing the CSI (Channel State Information) signal, extracting features, and building a machine-learning correlation model, the number and location of occupants can be estimated and further inferred in combination with sensing data such as vehicle temperature. Even better, with the computing power of the edge-side devices to process data in collaboration with the cloud, the computing process is partially localized to reduce the computing pressure and latency in the cloud. The approach has been experimentally verified to have more than 85% accuracy.  
CSI; ESP32; smart terminal; in-vehicle wireless sensing; personnel identification +
Footnote †: journal: Applied Sciences  
in a lightless environment. Previous Wi-Fi-aware devices have used routers or devices equipped with fixed-model wireless cards to collect data that cannot be put inside real vehicles. This drawback is overcome by the ESP32 device, which allows direct access to a wealth of other RF (Radio Frequency) signals, such as CSI, from the microcontroller [7]. There is a high degree of versatility in IoT-related wireless sensing applications, which can be achieved using only battery power.  
Based on the approach of using CSI sensing, we propose a contactless in-vehicle personnel sensing method, Car-Sense, based on ESP32. As shown in Figure 1, two ESP32 microcontrollers are installed in the front and rear of the interior space of a real vehicle. When both are active, the device can automatically initiate communication, and we can then collect CSI, RSSI, temperature, and other data for the detection and fully sense the various states of persons inside the vehicle in order to meet the low latency and high-agility real-time requirements needed for detection and also to reduce the computational pressure on the server, to allow the edge-end device at the source of data collection to integrate a series of core capabilities such as network computing and storage, to allow the device directly at the edge-end to undertake the task of data processing, and allow the cloud model to identify feedback more quickly. The main contributions of this paper are as follows.  
For dangerous events such as heat stroke, suffocation, or even death of persons left in the car, Car-Sense implements Device-free Wi-Fi Sensing with ESP32, which uses the characteristics of determining the number of people in the car as well as the location and temperature of the persons to derive the result of leaving/remaining. The module is very small compared to other commercial Wi-Fi devices, and the cost of the module is extremely low, requiring no additional hardware for the system to operate.  
The special model of Car-Sense Smart Side + Cloud localizes part of the computation process. The side end is not only used as a data collection device but also performs data cleaning and feature extraction locally; the cloud end performs machine-learning-related model calculations.  
The system utilizes self-built multi-person and multi-scene in-vehicle person perception data sets, sets up various evaluation metrics, and verifies the system's robust person location sensitivity and resolution through extensive experiments in different scenarios.