  Driver activity classification is crucial for ensuring road safety, with
applications ranging from driver assistance systems to autonomous vehicle
control transitions. In this paper, we present a novel approach leveraging
generalizable representations from vision-language models for driver activity
classification. Our method employs a Semantic Representation Late Fusion Neural
Network (SRLF-Net) to process synchronized video frames from multiple
perspectives. Each frame is encoded using a pretrained vision-language encoder,
and the resulting embeddings are fused to generate class probability
predictions. By leveraging contrastively-learned vision-language
representations, our approach achieves robust performance across diverse driver
activities. We evaluate our method on the Naturalistic Driving Action
Recognition Dataset, demonstrating strong accuracy across many classes. Our
results suggest that vision-language representations offer a promising avenue
for driver monitoring systems, providing both accuracy and interpretability
through natural language descriptors.
