Current methods of imitation learning (IL), primarily based on deep neural networks, offer efficient means for obtaining driving policies from real-world data but suffer from significant limitations in interpretability and generalizability. These shortcomings are particularly concerning in safety-critical applications like autonomous driving. In this paper, we address these limitations by introducing Symbolic Imitation Learning (SIL), a groundbreaking method that employs Inductive Logic Programming (ILP) to learn driving policies which are transparent, explainable and generalisable from available datasets. Utilizing the real-world highD dataset, we subject our method to a rigorous comparative analysis against prevailing neural-network-based IL methods. Our results demonstrate that SIL not only enhances the interpretability of driving policies but also significantly improves their applicability across varied driving situations. Hence, this work offers a novel pathway to more reliable and safer autonomous driving systems, underscoring the potential of integrating ILP into the domain of IL.

翻译：当前的模仿学习方法主要基于深度神经网络，为从现实数据获取驾驶策略提供了高效手段，但在可解释性和泛化能力方面存在显著局限性。这些缺陷在自动驾驶等安全关键应用中尤为令人担忧。本文通过引入符号模仿学习（SIL）——一种利用归纳逻辑编程（ILP）从可用数据集中学习透明、可解释且可泛化的驾驶策略的突破性方法——来解决上述局限性。我们利用真实世界的高D数据集，将所提方法与当前主流的基于神经网络的模仿学习方法进行了严格的比较分析。结果表明，SIL不仅增强了驾驶策略的可解释性，还显著提升了其在多样化驾驶情境中的适用性。因此，本研究为构建更可靠、更安全的自动驾驶系统提供了新路径，彰显了将ILP融入模仿学习领域的潜力。