CEMFormer: Learning to Predict Driver Intentions from In-Cabin and External Cameras via Spatial-Temporal Transformers (2305.07840v1)

Abstract: 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.