Driver Distraction Detection Based on Multi-scale Feature Fusion Network

Abstract

The occurrence of road traffic accidents has increased year by year.Driver inattention during driving is one of the major causes of traffic accidents.In this paper,we utilize multi-source data to detect driver distraction.However,the correlations derived from multi-source data will generate feature of high-dimensional entanglement.Existing methods perform similar processing for data of different sources or simply stick to concatenate multi-source features,which are not easy to catch the key feature of high-dimensional entanglement.And distracted driving can be affected by many factors.Supervised methods might cause misclassification when the type of driver distraction does not exist in the set of the known categories.Therefore,we propose a multi-dcale feature fusion network approach to tackle these challenges.Basically,it first learns low-dimensional representations from multi-source data through multiple embedding subnetworks,and then proposes a multi-scale feature Fusion method to aggregate these representations from the perspective of spatial-temporal correlation,thereby reducing the entanglement of feature.Finally,we utilize a ConvLSTM encoder-decoder model to detect driver distraction.Experimental results on a public loaded drive dataset show that the proposed method outperforms the existing methods.

Keywords

• driver distraction|unsupervised learning|multi-source|multi-scale fusion|encoder-decoder