記号的模倣学習：ブラックボックスから説明可能な運転ポリシーへ
Symbolic Imitation Learning: From Black-Box to Explainable Driving Policies
帰納論理プログラミングを用いて合成データから説明可能で汎化性の高い運転ポリシーを導出する記号的模倣学習フレームワークを提案し、実世界データセットで評価した。
著者: Iman Sharifi, Mustafa Yildirim, Saber Fallah
分類: cs.LG, cs.AI, cs.RO
原文アブストラクト
Current imitation learning approaches, predominantly based on deep neural networks (DNNs), offer efficient mechanisms for learning driving policies from real-world datasets. However, they suffer from inherent limitations in interpretability and generalizability--issues of critical importance in safety-critical domains such as autonomous driving. In this paper, we introduce Symbolic Imitation Learning (SIL), a novel framework that leverages Inductive Logic Programming (ILP) to derive explainable and generalizable driving policies from synthetic datasets. We evaluate SIL on real-world HighD and NGSim datasets, comparing its performance with state-of-the-art neural imitation learning methods using metrics such as collision rate, lane change efficiency, and average speed. The results indicate that SIL significantly enhances policy transparency while maintaining strong performance across varied driving conditions. These findings highlight the potential of integrating ILP into imitation learning to promote safer and more reliable autonomous systems.
