用LSTM筛选自动驾驶车道保持测试，更准更省时。
An LSTM-based Test Selection Method for Self-Driving Cars
- 将道路特征序列化，用LSTM分类测试用例安全与否。
- LSTM方法准确率和精确率优于传统机器学习方法。
- 适合自动驾驶仿真测试优化，尤其关注高难度场景。
自动驾驶汽车需大量测试，耗时昂贵。为优化流程，应剔除简单测试，聚焦挑战性场景。本研究针对自动驾驶车道保持系统的测试选择问题，提取道路段特征（如角度、长度）作为序列，利用长短期记忆网络（LSTM）模型对测试用例进行‘安全’或‘不安全’分类。相比基于机器学习的测试选择器，所提LSTM方法在准确率和精确率上表现更优，召回率和F1分数相当。该工作提出一种新型深度学习方法，有效解决自动驾驶仿真环境中的测试选择问题。
原文摘要 · Abstract (English)
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.
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