# Car-Sense: Vehicle Occupant Legacy Hazard Detection Method Based on DFWS
## 4 Method Design
### Personnel Status Identification
Considering that most of the vehicles people use in daily life are cars and SUVs, we also set up two kinds of environments for testing in the environment settings, such as ordinary cars and SUVs, which keep other experimental parameters such as the location of the equipment unchanged and carry out comparison experiments of different vehicle internal environments under the same parameters, and the comparison results are shown in Figure 13.

As can be seen from the figure, in both scenarios, the basic recognition results are in line with those described in 6.1. The overall average recognition effect of the number of people is above 70%, and the accuracy rate is more than 85% for people with 0 or 1. The average result for a single-person location can be 88%. Due to the high similarity of the interior environments of the two scenarios selected, the experimental results can prove that Car-Sense can be more effective in producing correct results in general vehicles, especially for dangerous environments with a single person inside the vehicle, with better recognition accuracy and robustness in the different tested environments. Figure 12: Three algorithms confusion matrix: (**a**) KNN confusion matrix; (**b**) SVM confusion matrix; (**c**) MLP confusion matrix. Here, 0, 1, 2, and 3 represent membership accuracy, and 4 represents positioning accuracy. ## 6 Conclusions

This paper presents Car-Sense, a lightweight, integrated ESP32-WiFi-based in-vehicle occupant sensing method that uses channel state information as the primary data source and is easy to deploy with good privacy compared to other sensing approaches, and addresses safety hazards caused by occupant retention through collaborative processing between local and cloud platforms. It has been shown to be unaffected by the presence of same-sized objects in the vehicle, to be more than 85% accurate for both adults and children, and to be robust across different vehicle types. However, the shortcomings of this system are that the recognition model is relatively simple, and the data set collected does not cover all vehicles. More efficient and simple solutions will be explored in the future. Conceptualization, G.W. and Z.H. ; methodology, G.W. and Z.H. ; software, data acquisition, and interpretation, G.W. ; writing--original draft preparation, G.W. ; critically revised the manuscript, Z.H., G.W. and X.D. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China (Grant 62262061, Grant 62162056), the Key Science and Technology Support Program of Gansu Province (Grant 20Y8 GA048), 2019 Chinese Academy of Sciences \"Light of the West\" Talent Program, Science and Technology Innovation Project of Gansu Province (Grant CX2 JA037, 17 CX2 JA039), 2019 Lanzhou City Science and Technology Plan Project (2019-4-44), 2020 Lanzhou City Talent Innovation and Entrepreneurship Project (2020-RC-116), and Gansu Provincial Department of Education: Industry Support Program Project (2022 CYZC-12). Not applicable. All participants in the experiment were informed and consented to the publication of the paper. Data Availability Statement: Data is contained within the article. The authors would like to thank all the participants and the school for their financial support. The authors declare no conflict of interest. ## References

* (1) [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A multi-agent system to avoid heatstroke in young children left in baby car seats inside vehicles. In Proceedings of the 2014 International Conference on Computational Science and Computational Intelligence, Las Vegas, NV, USA, 10-13 March 2014; IEEE: Piscataway, NJ, USA, 2014; Volume 2, pp. 245-248. * (2) [PERSON]; [PERSON] [PERSON]; [PERSON]; [PERSON]; [PERSON] Fatal heat stroke in children found in parked cars: Autopsy findings. _Eur. J. Pediatr._**2016**, _175_, 1249-1252. [CrossRef] [PubMed

Figure 13: Effect of different vehicle environments on recognition accuracy: (**a**) Vehicle; (**b**) SUV.