  Deep learning has been at the core of the autonomous driving field
development, due to the neural networks' success in finding patterns in raw
data and turning them into accurate predictions. Moreover, recent
neuro-symbolic works have shown that incorporating the available background
knowledge about the problem at hand in the loss function via t-norms can
further improve the deep learning models' performance. However, t-norm-based
losses may have very high memory requirements and, thus, they may be impossible
to apply in complex application domains like autonomous driving. In this paper,
we show how it is possible to define memory-efficient t-norm-based losses,
allowing for exploiting t-norms for the task of event detection in autonomous
driving. We conduct an extensive experimental analysis on the ROAD-R dataset
and show (i) that our proposal can be implemented and run on GPUs with less
than 25 GiB of available memory, while standard t-norm-based losses are
estimated to require more than 100 GiB, far exceeding the amount of memory
normally available, (ii) that t-norm-based losses improve performance,
especially when limited labelled data are available, and (iii) that
t-norm-based losses can further improve performance when exploited on both
labelled and unlabelled data.
