An efficient driving behavior prediction approach using physiological auxiliary and adaptive LSTM

Abstract

Driving behavior prediction is crucial in designing a modern Advanced driver assistance system (ADAS). Such predictions can improve driving safety by alerting the driver to the danger of unsafe or risky traffic situations. In this research, an efficient approach, Driver behavior network (DBNet) is proposed for driving behavior prediction using multiple modality data, i.e. front view video frames and driver physiological signals. Firstly, a Relation-guided spatial attention (RGSA) module is adopted to generate driving scene-centric features by modeling both local and global information from video frames. Secondly, a new Global shrinkage (GS) block is designed to incorporate soft thresholding as nonlinear transformation layer to generate physiological features and eliminate noise-related information from physiological signals. Finally, a customized Adaptive focal loss based Long short term memory (AFL-LSTM) network is introduced to learn the multi-modal features and capture the dependencies within driving behaviors simultaneously. We applied our approach on real data collected during drives in both urban and freeway environment in an instrumented vehicle. The experimental findings demonstrate that the DBNet can predict the upcoming driving behavior efficiently and significantly outperform other state-of-the-art models.