# Car-Sense: Vehicle Occupant Legacy Hazard Detection Method Based on DFWS
## 4 Method Design
### Personnel Status Identification
### Impact of Location Diversity

In order to investigate the effect of different installation positions of Car-Sense's wireless devices on the accuracy of detection, three different positions were set up in this paper, namely, the rear side and front side of the car, right B-pillar and the front side of the car, right B-pillar and left B-pillar, and packets were sent to RX via TX, and Figure 11a-c shows these three different device positions. Next, the relationship between accuracy and device deployment location is explored by determining the location information of personnel under different locations and the number of personnel information under different locations. The comparison results are shown in Figure 11d. As can be seen from Figure 11d above, when the transmitting and receiving position is Location 1, when the device is in the installation position shown in Figure 11a above, the average accuracy of the number of people and the average accuracy of the location of a single person both reach more than 80%, which has high robustness. When the device is at Locations 2 and 3, analysis of Figure 11b, c above shows that due to the complex environment with more interfering objects in the signal propagation path, the recognition effect is around 70%, which is lower than that of Location 1. The reason for this is that when the device is placed in front and behind, the person is more fully exposed to the coverage of the wireless signal, and the time domain of the CSI signal is more robust. The time domain of the CSI signal carries more information, and the classification accuracy is higher. In general, the device has the best recognition effect under Location 1, which on the other hand, also shows that Car-Sense still requires a certain level of location for the device. ### Impact of Different Classifiers

The performance of the classification algorithm is also an important indicator of the capability of the system. In order to verify the performance of the classification algorithm,

Figure 11: Comparison results of different device positions: (**a**–**c**) location of different equipment; (**d**) effect of different device locations on recognition accuracy. the data are processed using the method proposed in this paper and then processed by the nearest neighbor algorithm (KNN), support vector machine (SVM), and multilayer perceptron (MLP), respectively, and the confusion matrix of the comparison results obtained from the identification of the number of people and the location detection is shown in Figure 12.

Figure 12 shows the confusion matrix of the results obtained from the number of people recognized in a stationary vehicle using three different algorithms. Respectively. From the experimental results, we can see that the KNN algorithm has the best classification effect among the three algorithms, followed by SVM, and the recognition effect of MLP is the worst. Moreover, in the effect of personnel location recognition, the accuracy of MLP is only 58.4% which may be due to the problem of parameter setting at the beginning of training MLP, which does not achieve the optimal classification effect. Overall, due to the multipath effect, several experiments show higher recognition when the number of people is around zero, one, and the recognition decreases when the number of people is three and four. The comparison results prove that Car-Sense obtains the best results with the addition of the KNN algorithm. ### Impact of Different Environments

In the real scenario, different vehicles will also have a greater impact on the recognition results because they have different cabin environments.