Symbolic Imitation Learning: From Black-Box to Explainable Driving Policies

• URL: http://arxiv.org/abs/2309.16025v1
• Date: Wed, 27 Sep 2023 21:03:45 GMT
• Title: Symbolic Imitation Learning: From Black-Box to Explainable Driving Policies
• Authors: Iman Sharifi and Saber Fallah
• Abstract summary: We introduce Symbolic Learning (SIL) to learn driving policies which are transparent, explainable and generalisable from available datasets. Our results demonstrate that SIL not only enhances the interpretability of driving policies but also significantly improves their applicability across varied driving situations.
• Score: 5.977871949434069
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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