  Visual recognition inside the vehicle cabin leads to safer driving and more
intuitive human-vehicle interaction but such systems face substantial obstacles
as they need to capture different granularities of driver behaviour while
dealing with highly limited body visibility and changing illumination.
Multimodal recognition mitigates a number of such issues: prediction outcomes
of different sensors complement each other due to different modality-specific
strengths and weaknesses. While several late fusion methods have been
considered in previously published frameworks, they constantly feature
different architecture backbones and building blocks making it very hard to
isolate the role of the chosen late fusion strategy itself. This paper presents
an empirical evaluation of different paradigms for decision-level late fusion
in video-based driver observation. We compare seven different mechanisms for
joining the results of single-modal classifiers which have been both popular,
(e.g. score averaging) and not yet considered (e.g. rank-level fusion) in the
context of driver observation evaluating them based on different criteria and
benchmark settings. This is the first systematic study of strategies for fusing
outcomes of multimodal predictors inside the vehicles, conducted with the goal
to provide guidance for fusion scheme selection.
