Self-Driving Cars (SDCs) require extensive testing in a simulator, which can be costly in terms of time. To optimize the test process, simple and straightforward test cases should be excluded, while challenging test cases should be selected. This study addresses the test selection problem for lane-keeping systems of self-driving cars. Road segment features, such as angles and lengths, were extracted and treated as sequences, enabling classification of the test cases as PASS or FAIL using a Long Short-Term Memory (LSTM) model, named ITS4SDC. The ITS4SDC model is compared against a range of traditional machine learning-based classifiers. Results indicate that the ITS4SDC model outperforms machine learning-based methods in accuracy and precision while exhibiting comparable performance in recall and F1-score. A follow-up analysis demonstrated the robustness of the ITS4SDC model regarding changes in the Out-Of-Bound (OOB) measure which was used to distinguish PASS from FAIL cases. This work presents a novel LSTM-based approach that solves the problem of selecting test cases in the context of simulation-based testing of SDCs. The proposed solution is effective and robust.