Encoding and monitoring responsibility sensitive safety rules for automated vehicles in signal temporal logic

Abstract

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.