自動運転車のためのLSTMベースのテスト選択手法
An LSTM-based Test Selection Method for Self-Driving Cars
道路区間の特徴を系列データとして扱い、LSTMでテストケースを安全・危険に分類することで、自動運転の車線維持システムのテスト選択を効率化する手法を提案した。
著者: Ali Güllü, Faiz Ali Shah, Dietmar Pfahl
分類: cs.RO, cs.SE
原文アブストラクト
Self-driving cars require extensive testing, which can be costly in terms of time. To optimize this process, simple and straightforward tests should be excluded, focusing on challenging tests instead. This study addresses the test selection problem for lane-keeping systems for self-driving cars. Road segment features, such as angles and lengths, were extracted and treated as sequences, enabling classification of the test cases as "safe" or "unsafe" using a long short-term memory (LSTM) model. The proposed model is compared against machine learning-based test selectors. Results demonstrated that the LSTM-based method outperformed machine learning-based methods in accuracy and precision metrics while exhibiting comparable performance in recall and F1 scores. This work introduces a novel deep learning-based approach to the road classification problem, providing an effective solution for self-driving car test selection using a simulation environment.