为矿井无人机安全测试提供覆盖导向的系统化分析方法
SCALOFT: An Initial Approach for Situation Coverage-Based Safety Analysis of an Autonomous Aerial Drone in a Mine Environment
- 基于场景覆盖率构建安全测试框架，动态生成多样测试用例
- 通过实时监控与违规标识，成功检测出预设故障
- 适合从事自主飞行系统安全验证的研究者与工程师
自主系统在动态危险环境中的安全性面临重大挑战。本文提出一种名为SCALOFT的测试方法，用于系统性评估矿井环境中自主空中无人机的安全性。SCALOFT提供测试用例生成、系统行为实时监控及安全违规检测的完整框架，并将检测到的违规事件以唯一标识记录，便于后续分析与改进。该方法通过监测场景覆盖率并计算最终覆盖率指标，支撑安全论证。我们通过人为引入若干可复现的故障，评估SCALOFT的检测能力。在一组合理设定的故障中，结果显示该方法能够有效识别异常。
原文摘要 · Abstract (English)
The safety of autonomous systems in dynamic and hazardous environments poses significant challenges. This paper presents a testing approach named SCALOFT for systematically assessing the safety of an autonomous aerial drone in a mine. SCALOFT provides a framework for developing diverse test cases, real-time monitoring of system behaviour, and detection of safety violations. Detected violations are then logged with unique identifiers for detailed analysis and future improvement. SCALOFT helps build a safety argument by monitoring situation coverage and calculating a final coverage measure. We have evaluated the performance of this approach by deliberately introducing seeded faults into the system and assessing whether SCALOFT is able to detect those faults. For a small set of plausible faults, we show that SCALOFT is successful in this.
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