  Traditional video-based human activity recognition has experienced remarkable
progress linked to the rise of deep learning, but this effect was slower as it
comes to the downstream task of driver behavior understanding. Understanding
the situation inside the vehicle cabin is essential for Advanced Driving
Assistant System (ADAS) as it enables identifying distraction, predicting
driver's intent and leads to more convenient human-vehicle interaction. At the
same time, driver observation systems face substantial obstacles as they need
to capture different granularities of driver states, while the complexity of
such secondary activities grows with the rising automation and increased driver
freedom. Furthermore, a model is rarely deployed under conditions identical to
the ones in the training set, as sensor placements and types vary from vehicle
to vehicle, constituting a substantial obstacle for real-life deployment of
data-driven models. In this work, we present a novel vision-based framework for
recognizing secondary driver behaviours based on visual transformers and an
additional augmented feature distribution calibration module. This module
operates in the latent feature-space enriching and diversifying the training
set at feature-level in order to improve generalization to novel data
appearances, (e.g., sensor changes) and general feature quality. Our framework
consistently leads to better recognition rates, surpassing previous
state-of-the-art results of the public Drive&Act benchmark on all granularity
levels. Our code is publicly available at
https://github.com/KPeng9510/TransDARC.
