# A Two-Step E-Nose System for Vehicle Drunk Driving Rapid Detection
## 1 Introduction
The electronic nose used in the drunk driving detection system designed in this paper is a system that recognizes single or complex components of gas or odor by a specific sensor array combined with the corresponding pattern recognition algorithm [8]. In recent decades, researchers have applied the electronic nose system to many fields, such as food detection, air quality monitoring, wound bacteria detection, disease detection, and fire detection [9; 10; 11; 12; 13; 14], and achieved good results. For example, in terms of odor detection in confined spaces, the electronic nose has been used in the Mir space station to monitor variations in air quality within the station [15]. In terms of odor interference, an electronic nose based on the BP-ANN model has been successfully utilized to conduct semi-quantitative, real-time, and anti-interference detection of CO and CH\({}_{4}\) in contexts of H\({}_{2}\) and CH\({}_{2}\)O interference [16]. Exhaled gas after drinking is a kind of mixed gas, and there is usually interfering gas in the cabin, so the electronic nose is a suitable device for onboard drunk driving detection. In addition to the consideration of the detection effect, the user's experience and installation cost should also be taken into account in the design of the onboard alcohol driving detection system for shared vehicles. To avoid the problem that the traditional onboard ignition interlock device needs each driver to blow air for detection and the cost problem caused by the breath-based DADSS needing to install sensors at multiple locations, we propose a two-step drunk driving detection framework based on the electronic nose. The first step is to detect whether there is gas exhaled after drinking in the cabin to determine whether someone in the vehicle has drunk alcohol. Then, when it is determined that someone has drunk alcohol, the second step is to let the driver actively exhale into the electronic nose to judge whether the driver has drunk alcohol. This design reduces the requirements for the driver to a certain extent and ensures the user's experience. At the same time, the installation position of the sensor is reduced, and the cost is saved. In addition to proposing the two-step drunk driving detection framework, we also optimized the sensor array. The array composed of multiple sensors is the kernel component of the electronic nose. With the continuous development of material research, various gas-sensing materials are gradually developed, such as graphene [17], carbon nanotubes [18], conductive polymers [19], and metal oxides [20]. Among them, the metal oxide semiconductor (MOS) gas sensor has been widely used in the electronic nose, mainly because the MOS gas sensor has a fast response to the target gas, a long service life, mature production technology, a low price, and so on [21]. A remarkable feature of the array of MOS sensors in the electronic nose is cross-sensitivity, so the redundancy of the sensor array is inevitable. Sensor array optimization can remove redundant information, speed up data processing, and make the electronic nose use less cost and space, which helps the electronic nose drunk driving detection system be widely used in shared cars. This paper presents a kind of electronic nose system for drunk driving detection based on a MOS sensor array, which can be widely used in shared cars. The detection system applies a two-step alcohol driving detection framework proposed by us, which reduces interference to the driver and ensures the driver's user experience. The random forest algorithm is used to optimize the original sensor array, which reduces the cost and volume of the system. The optimal sampling time and detection algorithm are selected by time-slicing the data to ensure detection efficiency and accuracy. In the second part of this paper, we introduce our two-step detection framework in detail. The third part introduces the simulated exhalation device, electronic nose, test procedure, and optimization method of the sensor array.