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arXiv 1807.06172eess.SYcs.ROcs.SY
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开放源代码驾驶代理的实验韧性评估

Experimental Resilience Assessment of An Open-Source Driving Agent

Department of Electrical and Computer Engineering(电气与计算机工程系)

Abu Hasnat Mohammad Rubaiyat, Yongming Qin, Homa Alemzadeh

AI总结:

本文提出基于系统理论过程分析(STPA)的故障注入框架,用于评估开放源代码驾驶代理openpilot在不同环境条件和传感器数据故障下的韧性,通过战略性软件故障注入方法提高不安全场景的覆盖率,从而更有效地模拟安全关键故障并测试自动驾驶车辆。

摘要:

自动驾驶车辆(AV)依赖雷达和相机等传感器进行环境感知、路径规划和控制。随着自主性提高和与复杂环境的交互增加,对AV安全性和可靠性的关注日益增长。本文提出一种基于系统理论过程分析(STPA)的故障注入框架,用于评估开放源代码驾驶代理openpilot在不同环境条件和影响传感器数据的故障下的韧性。为了增加测试期间不安全场景的覆盖率,我们采用战略性软件故障注入方法,其中触发故障注入的触发器来源于系统高级危险分析中识别出的不安全场景。实验结果表明,所提出的战略性故障注入方法相比随机故障注入提高了危险覆盖率,从而有助于更有效地模拟安全关键故障并测试AV。此外,本文还提供了关于openpilot安全机制性能及其在及时检测和恢复故障输入能力方面的见解。

英文摘要:

Autonomous vehicles (AV) depend on the sensors like RADAR and camera for the perception of the environment, path planning, and control. With the increasing autonomy and interactions with the complex environment, there have been growing concerns regarding the safety and reliability of AVs. This paper presents a Systems-Theoretic Process Analysis (STPA) based fault injection framework to assess the resilience of an open-source driving agent, called openpilot, under different environmental conditions and faults affecting sensor data. To increase the coverage of unsafe scenarios during testing, we use a strategic software fault-injection approach where the triggers for injecting the faults are derived from the unsafe scenarios identified during the high-level hazard analysis of the system. The experimental results show that the proposed strategic fault injection approach increases the hazard coverage compared to random fault injection and, thus, can help with more effective simulation of safety-critical faults and testing of AVs. In addition, the paper provides insights on the performance of openpilot safety mechanisms and its ability in timely detection and recovery from faulty inputs.

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。

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