Gated Driver Attention Predictor
Tianci Zhao Xue Bai Jianwu Fang Jianru Xue
Driver attention prediction implies the intention understanding of where the
driver intends to go and what object the driver concerned about, which commonly
provides a driving task-guided traffic scene understanding. Some recent works
explore driver attention prediction in critical or accident scenarios and find
a positive role in helping accident prediction, while the promotion ability is
constrained by the prediction accuracy of driver attention maps. In this work,
we explore the network connection gating mechanism for driver attention
prediction (Gate-DAP). Gate-DAP aims to learn the importance of different
spatial, temporal, and modality information in driving scenarios with various
road types, occasions, and light and weather conditions. The network connection
gating in Gate-DAP consists of a spatial encoding network gating,
long-short-term memory network gating, and information type gating modules.
Each connection gating operation is plug-and-play and can be flexibly
assembled, which makes the architecture of Gate-DAP transparent for evaluating
different spatial, temporal, and information types for driver attention
prediction. Evaluations on DADA-2000 and BDDA datasets verify the superiority
of the proposed method with the comparison with state-of-the-art approaches.
The code is available on https://github.com/JWFangit/Gate-DAP.