The dynamic nature of driving environments and the presence of diverse road
users pose significant challenges for decision-making in autonomous driving.
Deep reinforcement learning (DRL) has emerged as a popular approach to tackle
this problem. However, the application of existing DRL solutions is mainly
confined to simulated environments due to safety concerns, impeding their
deployment in real-world. To overcome this limitation, this paper introduces a
novel neuro-symbolic model-free DRL approach, called DRL with Symbolic Logics
(DRLSL) that combines the strengths of DRL (learning from experience) and
symbolic first-order logics knowledge-driven reasoning) to enable safe learning
in real-time interactions of autonomous driving within real environments. This
innovative approach provides a means to learn autonomous driving policies by
actively engaging with the physical environment while ensuring safety. We have
implemented the DRLSL framework in autonomous driving using the highD dataset
and demonstrated that our method successfully avoids unsafe actions during both
the training and testing phases. Furthermore, our results indicate that DRLSL
achieves faster convergence during training and exhibits better
generalizability to new driving scenarios compared to traditional DRL methods.Comment: 15 pages, 9 figures, 1 table, 1 algorithm. Under review as a journal
paper at IEEE transactions on Intelligent Transportation System