School of Electric and Information, Southwest Petroleum University, Chengdu 610500, China.
School of Mechatronic Engineering, Southwest Petroleum University, Chengdu 610500, China.
Sensors (Basel). 2022 Nov 3;22(21):8460. doi: 10.3390/s22218460.
Since drunk driving poses a significant threat to road traffic safety, there is an increasing demand for the performance and dependability of online drunk driving detection devices for automobiles. However, the majority of current detection devices only contain a single sensor, resulting in a low degree of detection accuracy, erroneous judgments, and car locking. In order to solve the problem, this study firstly designed a sensor array based on the gas diffusion model and the characteristics of a car steering wheel. Secondly, the data fusion algorithm is proposed according to the data characteristics of the sensor array on the steering wheel. The support matrix is used to improve the data consistency of the single sensor data, and then the adaptive weighted fusion algorithm is used for multiple sensors. Finally, in order to verify the reliability of the system, an online intelligent detection device for drunk driving based on multi-sensor fusion was developed, and three people using different combinations of drunk driving simulation experiments were conducted. According to the test results, a drunk person in the passenger seat will not cause the system to make a drunk driving determination. When more than 50 mL of alcohol is consumed and the driver is seated in the driver's seat, the online intelligent detection of drunk driving can accurately identify drunk driving, and the car will lock itself as soon as a real-time online voice prompt is heard. This study enhances and complements theories relating to data fusion for online automobile drunk driving detection, allowing for the online identification of drivers who have been drinking and the locking of their vehicles to prevent drunk driving. It provides technical support for enhancing the accuracy of online systems that detect drunk driving in automobiles.
由于酒后驾车对道路交通安全构成重大威胁，因此对汽车在线酒驾检测设备的性能和可靠性的需求日益增加。然而，目前大多数检测设备仅包含单个传感器，导致检测精度低、误判和汽车锁定。为了解决这个问题，本研究首先基于气体扩散模型和方向盘的特点设计了一个传感器阵列。其次，根据方向盘上传感器阵列的数据特点提出了数据融合算法。支持矩阵用于提高单个传感器数据的一致性，然后使用自适应加权融合算法对多个传感器进行融合。最后，为了验证系统的可靠性，开发了一种基于多传感器融合的在线智能酒驾检测装置，并对三人使用不同的酒驾模拟实验组合进行了测试。根据测试结果，坐在乘客座位上的醉酒者不会导致系统做出酒驾判断。当驾驶员坐在驾驶座上并饮用超过 50 毫升的酒精时，在线智能酒驾检测可以准确识别酒驾，并且一旦听到实时在线语音提示，汽车就会自动锁定。本研究增强和补充了与在线汽车酒驾检测的数据融合相关的理论，实现了对饮酒司机的在线识别和车辆锁定，以防止酒驾。它为提高在线汽车酒驾检测系统的准确性提供了技术支持。
