Deep driver behavior detection model based on human brain consolidated learning for shared autonomy systems

Abstract

To achieve efficient shared autonomy, driver behavior detection (DBD) is undoubtedly required. This paper investigates a deep driver behavior detection (DDBD) model. To overcome the low accuracy of DBD due to a lack of driver behavior data, the similarity of some driver behavior characteristics, and the ignorance of multi-scale structure and texture information, a DDBD model based on human brain consolidated learning (HBCL) is proposed. First, multiple DBD models with input information of different scales are trained based on transfer learning. Then, a new model called consolidation training (CT) using the Mish is trained based on the weight data from the first step. Finally, a novel method for the visualization of the attention area is proposed. The experimental results demonstrate that the proposed model achieved the highest accuracy (94.72% on the Kaggle-driving test dataset), generalization and real-time performance, the attention area is more anthropomorphic as compared with existing state-of-the-art models.