Fusion of Gaze and Scene Information for Driving Behaviour Recognition
A Graph-Neural-Network- Based Framework
Yangtian Yi (Beijing Institute of Technology)
Chao Lu (Beijing Institute of Technology)
Boyang Wang (Beijing Institute of Technology)
Long Cheng (Southeast University)
Zirui Li (TU Delft - Transport and Planning, Beijing Institute of Technology)
Jianwei Gong (Beijing Institute of Technology)
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Abstract
Accurate recognition of driver behaviours is the basis for a reliable driver assistance system. This paper proposes a novel fusion framework for driver behaviour recognition that utilises the traffic scene and driver gaze information. The proposed framework is based on the graph neural network (GNN) and contains three modules, namely, the gaze analysing (GA) module, scene understanding (SU) module and the information fusion (IF) module. The GA module is used to obtain gaze images of drivers, and extract the gaze features from the images. The SU module provides trajectory predictions for surrounding vehicles, motorcycles, bicycles and other traffic participants. The GA and SU modules are parallel and the outputs of both modules are sent to the IF module that fuses the gaze and scene information using the attention mechanism and recognises the driving behaviours through a combined classifier. The proposed framework is verified on a naturalistic driving dataset. The comparative experiments with the state-of-the-art methods demonstrate that the proposed framework has superior performance for driving behaviour recognition in various situations.