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.

翻译：驾驶环境的动态性以及多样化道路参与者的存在，给自动驾驶决策带来了重大挑战。深度强化学习（DRL）已成为解决该问题的热门方法。然而，由于安全问题，现有DRL方案的应用主要局限于模拟环境，阻碍了其在现实世界的部署。为克服这一局限，本文提出一种新颖的神经符号无模型DRL方法，称为DRL与符号逻辑（DRLSL），该方法结合了DRL（从经验中学习）与符号一阶逻辑（知识驱动推理）的优势，从而能够在现实环境的自动驾驶实时交互中实现安全学习。这种创新方法提供了一种方式，通过主动与物理环境交互来学习自动驾驶策略，同时确保安全性。我们利用highD数据集在自动驾驶中实现了DRLSL框架，并证明该方法在训练和测试阶段均能成功避免不安全行为。此外，结果表明，与传统DRL方法相比，DRLSL在训练中收敛更快，并对新驾驶场景展现出更强的泛化能力。