Vehicle Emotion Monitoring System of Drugged Driving Personnel Based on GWO-SVM Model

Abstract

In recent years, drug addicts have gradually increased, and drugged driving incidents have happened occasionally. The discovery and control of drugged driving personnel has always been perplexing the traffic management department. In fact, there are significant differences in emotion between drug addicts and normal people. Based on vibraimage technology and quantitative emotion data, this paper established a real-time vehicle emotion monitoring system for drugged driving personnel, which used support vector machine (SVM) with GWO as the core algorithm to realize the classification and recognition of drugged driving personnel. The experimental results show that, compared with SVM models optimized by other algorithms, this model has obvious advantages in predicting drug addicts’ recognition accuracy and operating efficiency, and the established GWO-SVM model has excellent learning ability, generalization performance and stability, which can greatly meet the analysis requirements of vehicle emotion monitoring system for drugged driving personnel. The proposal of vehicle emotion monitoring system of drugged driving personnel and core module algorithm provides technical support and application prospect for discovering potential drugged driving personnel in time.