Neural Architecture Search for Real-Time Driver Behavior Recognition

Driver behavior recognition (DBR) helps to ensure driver safety by alerting drivers about potential hazards and minimizing them. In this paper, we use deep learning-based neural architecture search (NAS) to classify driver behavior. In the NAS method, a reinforcement learning algorithm is used, and the neural network architecture is quickly searched by sharing the weights of the parameters. Most DBR models focus on accuracy, while high processing speed is required in order to be applied to actual vehicles. In addition, since the driver monitoring system (DMS) includes complex algorithms based on deep learning, it requires a DBR model that takes this into account. We collect our own data set for driver behavior classification and recognize four common driving behaviors: general driving, mobile phone use, food intake, and smoking. The proposed model on our own data set collected through experiments has better performance and lower network cost than the previous lightweight classification model.