1 research outputs found
A Unified Multi-scale and Multi-task Learning Framework for Driver Behaviors Reasoning
Mutual understanding between driver and vehicle is critically important to
the design of intelligent vehicles and customized interaction interface. In
this study, a unified driver behavior reasoning system toward multi-scale and
multi-tasks behavior recognition is proposed. Specifically, a multi-scale
driver behavior recognition system is designed to recognize both the driver's
physical and mental states based on a deep encoder-decoder framework. This
system can jointly recognize three driver behaviors with different time scales
based on the shared encoder network. Driver body postures and mental behaviors
include intention and emotion are studied and identified. The encoder network
is designed based on a deep convolutional neural network (CNN), and several
decoders for different driver states estimation are proposed with fully
connected (FC) and long short-term memory (LSTM) based recurrent neural
networks (RNN). The joint feature learning with the CNN encoder increases the
computational efficiency and feature diversity, while the customized decoders
enable an efficient multi-tasks inference. The proposed framework can be used
as a solution to exploit the relationship between different driver states, and
it is found that when drivers generate lane change intentions, their emotions
usually keep neutral state and more focus on the task. Two naturalistic
datasets are used to investigate the model performance, which is a local
highway dataset, namely, CranData and one public dataset from Brain4Cars. The
testing results on these two datasets show accurate performance and outperform
existing methods on driver postures, intention, and emotion recognition