TY - GEN
T1 - Cooperative driving of connected autonomous vehicles using responsibility-sensitive safety (RSS) rules
AU - Khayatian, Mohammad
AU - Mehrabian, Mohammadreza
AU - Allamsetti, Harshith
AU - Liu, Kai Wei
AU - Huang, Po Yu
AU - Lin, Chung Wei
AU - Shrivastava, Aviral
N1 - Funding Information:
This work was partially supported by funding from NIST Award 70NANB19H144, and by National Science Foundation grants CNS 1525855 and CPS 1645578. This work was also partially supported by MOE and MOST in Taiwan under Grant Numbers NTU-109V0901, MOST-109-2636-E-002-022, and MOST-110-2636-E-002-026.
Publisher Copyright:
© 2021 ACM.
PY - 2021/5/19
Y1 - 2021/5/19
N2 - Connected Autonomous Vehicles (CAVs) are expected to enable reliable and efficient transportation systems. Most motion planning algorithms for multi-agent systems are not completely safe because they implicitly assume that all vehicles/agents will execute the expected plan with a small error. This assumption, however, is hard to keep for CAVs since they may have to slow down (e.g., to yield to a jaywalker) or are forced to stop (e.g. break down), sometimes even without a notice. Responsibility-Sensitive Safety (RSS) defines a set of safety rules for each driving scenario to ensure that a vehicle will not cause an accident irrespective of other vehicles' behavior. RSS rules, however, are hard to evaluate for merge, intersection, and unstructured road scenarios. In addition, deadlock situations can happen that are not considered by the RSS. In this paper, we propose a generic version of RSS rules for CAVs that can be applied to any driving scenario. We integrate the proposed RSS rules with the CAV's motion planning algorithm to enable cooperative driving of CAVs. Our approach can also detect and resolve deadlocks in a decentralized manner. We have conducted experiments to verify that a CAV does not cause an accident no matter when other CAVs slow down or stop. We also showcase our deadlock detection and resolution mechanism. Finally, we compare the average velocity and fuel consumption of vehicles when they drive autonomously but not connected with the case that they are connected.
AB - Connected Autonomous Vehicles (CAVs) are expected to enable reliable and efficient transportation systems. Most motion planning algorithms for multi-agent systems are not completely safe because they implicitly assume that all vehicles/agents will execute the expected plan with a small error. This assumption, however, is hard to keep for CAVs since they may have to slow down (e.g., to yield to a jaywalker) or are forced to stop (e.g. break down), sometimes even without a notice. Responsibility-Sensitive Safety (RSS) defines a set of safety rules for each driving scenario to ensure that a vehicle will not cause an accident irrespective of other vehicles' behavior. RSS rules, however, are hard to evaluate for merge, intersection, and unstructured road scenarios. In addition, deadlock situations can happen that are not considered by the RSS. In this paper, we propose a generic version of RSS rules for CAVs that can be applied to any driving scenario. We integrate the proposed RSS rules with the CAV's motion planning algorithm to enable cooperative driving of CAVs. Our approach can also detect and resolve deadlocks in a decentralized manner. We have conducted experiments to verify that a CAV does not cause an accident no matter when other CAVs slow down or stop. We also showcase our deadlock detection and resolution mechanism. Finally, we compare the average velocity and fuel consumption of vehicles when they drive autonomously but not connected with the case that they are connected.
KW - city-wide traffic management
KW - connected autonomous vehicles
KW - intelligent transportation systems
UR - http://www.scopus.com/inward/record.url?scp=85104173045&partnerID=8YFLogxK
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U2 - 10.1145/3450267.3450530
DO - 10.1145/3450267.3450530
M3 - Conference contribution
AN - SCOPUS:85104173045
T3 - ICCPS 2021 - Proceedings of the 2021 ACM/IEEE 12th International Conference on Cyber-Physical Systems (with CPS-IoT Week 2021)
SP - 11
EP - 20
BT - ICCPS 2021 - Proceedings of the 2021 ACM/IEEE 12th International Conference on Cyber-Physical Systems (with CPS-IoT Week 2021)
PB - Association for Computing Machinery, Inc
T2 - 12th ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS 2021, part of CPS-IoT Week 2021
Y2 - 19 May 2021 through 21 May 2021
ER -