自动驾驶汽车的仿真测试如何匹配人类感知？
How does Simulation-based Testing for Self-driving Cars match Human Perception?
AI总结：
本研究针对自动驾驶汽车仿真测试的越界质量指标与人类感知匹配度不明的问题，开发VR框架SDC-Alabaster开展50人实证研究，发现测试复杂度、交互可能性、年龄等会影响人类对测试用例安全性与真实感的评估，凸显相关研究的必要性。
摘要：
覆盖率、变异得分等软件指标已被广泛用于测试套件的自动化质量评估。传统工具依赖这类可量化的软件指标，而自动驾驶汽车（SDCs）领域主要聚焦于基于仿真的测试用例生成，采用越界（OOB）参数等质量指标判定测试用例是否通过。但目前尚不清楚该质量指标与人类对SDC安全性、真实感的感知匹配程度，而这两点是评估SDC行为的关键维度。为填补这一空白，我们开展了一项包含50名参与者的实证研究，探究哪些因素决定了人类对SDC测试用例“安全/不安全”“真实/不真实”的感知。为此，我们开发了一个利用虚拟现实（VR）技术的框架SDC-Alabaster，让研究参与者沉浸在SDC仿真器的虚拟环境中。研究结果表明，人类对通过/失败测试用例的安全性与真实感评估会受测试复杂度、与SDC交互的可能性等不同因素影响。尤其在真实感评估方面，参与者年龄作为混杂因素会导致感知差异。本研究凸显了开展更多SDC仿真测试质量指标研究的必要性，以及人类感知在SDC行为评估中的重要性。
英文摘要：
Software metrics such as coverage and mutation scores have been extensively explored for the automated quality assessment of test suites. While traditional tools rely on such quantifiable software metrics, the field of self-driving cars (SDCs) has primarily focused on simulation-based test case generation using quality metrics such as the out-of-bound (OOB) parameter to determine if a test case fails or passes. However, it remains unclear to what extent this quality metric aligns with the human perception of the safety and realism of SDCs, which are critical aspects in assessing SDC behavior. To address this gap, we conducted an empirical study involving 50 participants to investigate the factors that determine how humans perceive SDC test cases as safe, unsafe, realistic, or unrealistic. To this aim, we developed a framework leveraging virtual reality (VR) technologies, called SDC-Alabaster, to immerse the study participants into the virtual environment of SDC simulators. Our findings indicate that the human assessment of the safety and realism of failing and passing test cases can vary based on different factors, such as the test's complexity and the possibility of interacting with the SDC. Especially for the assessment of realism, the participants' age as a confounding factor leads to a different perception. This study highlights the need for more research on SDC simulation testing quality metrics and the importance of human perception in evaluating SDC behavior.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
