TY - GEN
T1 - Encoding and monitoring responsibility sensitive safety rules for automated vehicles in signal temporal logic
AU - Hekmatnejad, Mohammad
AU - Yaghoubi, Shakiba
AU - Dokhanchi, Adel
AU - Amor, Heni Ben
AU - Shrivastava, Aviral
AU - Karam, Lina
AU - Fainekos, Georgios
N1 - Funding Information:
This work was supported in part by NSF award 1350420 and by a gift from Intel Corporation.
Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/10/9
Y1 - 2019/10/9
N2 - 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.
AB - 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.
KW - Monitoring
KW - Responsibility-Sensitive Safety
KW - Robustness
KW - Signal-Temporal Logic
UR - http://www.scopus.com/inward/record.url?scp=85076686981&partnerID=8YFLogxK
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U2 - 10.1145/3359986.3361203
DO - 10.1145/3359986.3361203
M3 - Conference contribution
AN - SCOPUS:85076686981
T3 - MEMOCODE 2019 - 17th ACM-IEEE International Conference on Formal Methods and Models for System Design
BT - MEMOCODE 2019 - 17th ACM-IEEE International Conference on Formal Methods and Models for System Design
PB - Association for Computing Machinery, Inc
T2 - 17th ACM-IEEE International Conference on Formal Methods and Models for System Design, MEMOCODE 2019
Y2 - 9 October 2019 through 11 October 2019
ER -