用隐空间优化测试表示，让自动驾驶系统测试更高效发现漏洞。
Representation Improvement in Latent Space for Search-Based Testing of Autonomous Robotic Systems
- 将测试场景映射到变分自编码器的隐空间，提升表示能力。
- 相比基线方法，可多发现3到4.6倍的系统故障。
- 适合自动驾驶、无人机等复杂系统的测试验证工作。
自动驾驶机器人系统（如自动驾驶汽车和无人机）的测试因环境高度不可预测而困难。尽管仿真测试降低了真实世界风险，但因可能的测试场景空间庞大，仍耗时且资源密集。现有基于搜索的测试生成方法在场景表示方面改进有限。本文提出RILaST（Representation Improvement in Latent Space for Search-Based Testing）方法，通过将测试场景映射至变分自编码器（VAE）的隐空间来增强表示。在自主无人机和车道保持辅助系统两个用例上评估表明，RILaST比基线方法多发现3至4.6倍的故障，同时实现高测试多样性。
原文摘要 · Abstract (English)
Testing autonomous robotic systems, such as self-driving cars and unmanned aerial vehicles, is challenging due to their interaction with highly unpredictable environments. A common practice is to first conduct simulation-based testing, which, despite reducing real-world risks, remains time-consuming and resource-intensive due to the vast space of possible test scenarios. A number of search-based approaches were proposed to generate test scenarios more efficiently. A key aspect of any search-based test generation approach is the choice of representation used during the search process. However, existing methods for improving test scenario representation remain limited. We propose RILaST (Representation Improvement in Latent Space for Search-Based Testing) approach, which enhances test representation by mapping it to the latent space of a variational autoencoder. We evaluate RILaST on two use cases, including autonomous drone and autonomous lane-keeping assist system. The obtained results show that RILaST allows finding between 3 to 4.6 times more failures than baseline approaches, achieving a high level of test diversity.
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