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.

翻译：深度学习一直是自动驾驶领域发展的核心，因为神经网络在原始数据中发现模式并将其转化为准确预测方面取得了成功。此外，最近的神经符号研究表明，通过T-范数将关于问题的可用背景知识纳入损失函数，可以进一步提升深度学习模型的性能。然而，基于T-范数的损失函数可能具有非常高的内存需求，因此可能难以应用于像自动驾驶这样复杂的应用领域。在本文中，我们展示了如何定义内存高效的基于T-范数的损失函数，从而能够利用T-范数进行自动驾驶中的事件检测任务。我们在ROAD-R数据集上进行了广泛的实验分析，结果表明：(i) 我们的方案可以在可用内存小于25 GiB的GPU上实现和运行，而标准的基于T-范数的损失函数估计需要超过100 GiB，远超过通常可用的内存量；(ii) 基于T-范数的损失函数能提升性能，尤其是在标注数据有限的情况下；(iii) 当在标注和未标注数据上同时利用时，基于T-范数的损失函数可以进一步提升性能。