# A Two-Step E-Nose System for Vehicle Drunk Driving Rapid Detection
## 4 Experimental Results
### Drunk Driving Detection Results  
After obtaining the optimal sensor array and the optimal features, the optimal detection time and classification algorithm were selected. Considering that most drivers will not drive after drinking, a long drunk driving detection time in shared cars will cause unnecessary trouble to non-drinking car rental drivers. In order to obtain the optimal detection time that can ensure a fast and accurate detection task, we sliced the experimental data.  
We sliced and extracted the data with sampling times of 5 s, 10 s, 15 s, 20 s, 25 s, and 30 s from the original data of nine test conditions in the cabin gas detection test. According to the WT characteristics of these slice data, KNN, SVM, and RF algorithm classification models are established to judge whether there is exhaled gas after drinking in the cabin. Table 8 shows the classification accuracy of the three algorithm models using different slice time data.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Rank** & **Sensor** & **FT** & **IV** & **MAX** & **WT** & **Sum** \\ \hline
1 & TGS2620 & 0.026 & 0.139 & 0.147 & 0.147 & 0.459 \\
2 & TGS2611 & 0.017 & 0.128 & 0.125 & 0.129 & 0.399 \\
3 & TGS2600 & 0.011 & 0.106 & 0.105 & 0.101 & 0.323 \\
4 & TGS2602 & 0.012 & 0.072 & 0.096 & 0.022 & 0.202 \\
5 & MP-4 & 0.012 & 0.069 & 0.053 & 0.067 & 0.201 \\
6 & MP135 & 0.008 & 0.064 & 0.050 & 0.060 & 0.182 \\
7 & TGS2610 & 0.017 & 0.054 & 0.051 & 0.059 & 0.181 \\
8 & GSBT11 & 0.008 & 0.038 & 0.044 & 0.039 & 0.129 \\
9 & WSP7110 & 0.009 & 0.035 & 0.042 & 0.043 & 0.129 \\
10 & MP-9 & 0.010 & 0.030 & 0.046 & 0.040 & 0.126 \\
11 & TGS2612 & 0.005 & 0.040 & 0.037 & 0.040 & 0.122 \\ \hline \end{tabular}
\end{table}
Table 6: Overall importance ranking of sensors.  
\begin{table}
\begin{tabular}{c c c} \hline
**The Number of Sensors** & **Feature’s Type** & **Recognition Rate** \\ \hline
3 & WT & 0.981 \\
4 & IV & 0.992 \\
5 & MAX & 0.992 \\
6 & MAX & 0.994 \\
7 & WT & 0.997 \\
8 & WT & 1.000 \\
9 & IV & 0.994 \\
10 & WT & 0.997 \\
11 & WT & 0.994 \\ \hline \end{tabular}
\end{table}
Table 7: Classification accuracy of the sensor subset RF model test set.  
From Table 8, we can see that the best result is the SVM model built with the slice data with a sampling time of 25 s and the RF model built with the slice data with a sampling time of 15 s, both with an accuracy of 99.72%. However, considering the driver's experience, the detection time should be reduced as much as possible. At the same time, when the slice time is 5 s, the accuracy of the SVM model and RF model is 99.44%, which is not much different from the highest accuracy. So finally, in the first step of the alcohol detection framework, we choose 5 s as the sampling time and SVM as the classification algorithm.  
Similarly, we sliced and extracted the data with sampling times of 5 s, 10 s, 15 s, 20 s, 25 s, and 30 s from the original data of three cases of the direct expiratory test and established KNN, SVM, and RF algorithm classification models for the WT characteristics of these slice data to judge whether the driver has been drinking. Table 9 shows the classification accuracy of the three algorithm models using different slice time data.  
It can be seen from Table 9 that, using different slice time data, the accuracy of each model has reached 100% at 5 s. This may be due to the obvious difference between the components of exhalation before and after drinking. In the follow-up work, we will further subdivide the alcohol content in exhalation after drinking. However, to some extent, the results can still show that when the driver actively blows, the electronic nose can detect whether the driver is drunk. Finally, in the second step of the two-step drunk driving detection framework, we choose 5 s as the sampling time and SVM as the classification algorithm.