  Driver intention prediction seeks to anticipate drivers' actions by analyzing
their behaviors with respect to surrounding traffic environments. Existing
approaches primarily focus on late-fusion techniques, and neglect the
importance of maintaining consistency between predictions and prevailing
driving contexts. In this paper, we introduce a new framework called Cross-View
Episodic Memory Transformer (CEMFormer), which employs spatio-temporal
transformers to learn unified memory representations for an improved driver
intention prediction. Specifically, we develop a spatial-temporal encoder to
integrate information from both in-cabin and external camera views, along with
episodic memory representations to continuously fuse historical data.
Furthermore, we propose a novel context-consistency loss that incorporates
driving context as an auxiliary supervision signal to improve prediction
performance. Comprehensive experiments on the Brain4Cars dataset demonstrate
that CEMFormer consistently outperforms existing state-of-the-art methods in
driver intention prediction.
