Encoding and monitoring responsibility sensitive safety rules for automated vehicles in signal temporal logic
Encoding and monitoring responsibility sensitive safety rules for automated vehicles in signal temporal logic
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DOI：
10.1145/3359986.3361203
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发表时间：
2019-10
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通讯作者：
Mohammad Hekmatnejad;Shakiba Yaghoubi;Adel Dokhanchi;H. B. Amor;Aviral Shrivastava;Lina Karam;Georgios Fainekos
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作者：
Mohammad Hekmatnejad;Shakiba Yaghoubi;Adel Dokhanchi;H. B. Amor;Aviral Shrivastava;Lina Karam;Georgios Fainekos
随着自动驾驶汽车（AV）准备在无人监督的情况下行驶在公共道路上，许多实际问题仍然悬而未决。例如，对于什么是安全驾驶，没有一个普遍接受的正式定义。安全驾驶的正式定义可以用于开发车辆行为以及认证和法律的案件。为了实现这一目标，责任敏感的安全（RSS）模型被开发作为正式化安全驾驶行为的第一步，更广泛的AV社区可以在此基础上扩展。在本文中，我们证明了RSS模型可以在信号时序逻辑（STL）编码。此外，使用的S-TaLiRo工具，我们提出了一个案例研究的监控RSS的要求，从CommonRoad选定的交通场景。我们的结论是，即使在交通繁忙的情况下，监测的STL编码的RSS规则是有效的。一个有趣的观察是，对于选定的交通数据，车辆参数和响应时间，RSS模型违规并不频繁。
As Automated Vehicles (AV) get ready to hit the public roads unsupervised, many practical questions still remain open. For example, there is no commonly acceptable formal definition of what safe driving is. A formal definition of safe driving can be utilized in developing the vehicle behaviors as well as in certification and legal cases. Toward that goal, the Responsibility-Sensitive Safety (RSS) model was developed as a first step toward formalizing safe driving behavior upon which the broader AV community can expand. In this paper, we demonstrate that the RSS model can be encoded in Signal Temporal Logic (STL). Moreover, using the S-TaLiRo tools, we present a case study of monitoring RSS requirements on selected traffic scenarios from CommonRoad. We conclude that monitoring RSS rules encoded in STL is efficient even in heavy traffic scenarios. One interesting observation is that for the selected traffic data, vehicle parameters and response times, the RSS model violations are not frequent.