Traffic rule formalization for autonomous vehicle
This study devised and implemented a Defeasible Deontic Logic (DDL)-based formalization approach for translating traffic rules into a machine-computable (M/C) format and thus solving rule issues: rule vagueness (open texture expressions) and exceptions in rules. The resulting M/C format of traffic r...
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2022
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sg-smu-ink.cclaw-10052023-03-22T07:15:26Z Traffic rule formalization for autonomous vehicle BHUIYAN, Hanif GOVERNATORI, Guido RAKOTONIRAINY, Andry WONG, Meng Weng MAHAJAN, Avishkar This study devised and implemented a Defeasible Deontic Logic (DDL)-based formalization approach for translating traffic rules into a machine-computable (M/C) format and thus solving rule issues: rule vagueness (open texture expressions) and exceptions in rules. The resulting M/C format of traffic rules can be utilized for automatic traffic rule reasoning to assist the Autonomous Vehicle (AV) in making legal decisions. The method incorporates the components and behaviour of regulations based on the rule's obligation, prohibition, and permission activities. The need for the encoding methodology is motivated by the desire for automated reasoning over Autonomous Vehicle information involving traffic rules. A Queensland (QLD) overtaking traffic rule is used as a use case to illustrate this proposed encoding methodology’s mechanism and usefulness. 2022-12-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/cclaw/6 https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1005&context=cclaw http://creativecommons.org/licenses/by-nc-nd/4.0/ Centre for Computational Law eng Institutional Knowledge at Singapore Management University Traffic Rules Norms Defeasible Deontic Logic Transportation Law |
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Traffic Rules Norms Defeasible Deontic Logic Transportation Law BHUIYAN, Hanif GOVERNATORI, Guido RAKOTONIRAINY, Andry WONG, Meng Weng MAHAJAN, Avishkar Traffic rule formalization for autonomous vehicle |
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This study devised and implemented a Defeasible Deontic Logic (DDL)-based formalization approach for translating traffic rules into a machine-computable (M/C) format and thus solving rule issues: rule vagueness (open texture expressions) and exceptions in rules. The resulting M/C format of traffic rules can be utilized for automatic traffic rule reasoning to assist the Autonomous Vehicle (AV) in making legal decisions. The method incorporates the components and behaviour of regulations based on the rule's obligation, prohibition, and permission activities.
The need for the encoding methodology is motivated by the desire for automated reasoning over Autonomous Vehicle information involving traffic rules.
A Queensland (QLD) overtaking traffic rule is used as a use case to illustrate this proposed encoding methodology’s mechanism and usefulness. |
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BHUIYAN, Hanif GOVERNATORI, Guido RAKOTONIRAINY, Andry WONG, Meng Weng MAHAJAN, Avishkar |
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BHUIYAN, Hanif GOVERNATORI, Guido RAKOTONIRAINY, Andry WONG, Meng Weng MAHAJAN, Avishkar |
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BHUIYAN, Hanif |
title |
Traffic rule formalization for autonomous vehicle |
title_short |
Traffic rule formalization for autonomous vehicle |
title_full |
Traffic rule formalization for autonomous vehicle |
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Traffic rule formalization for autonomous vehicle |
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Traffic rule formalization for autonomous vehicle |
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traffic rule formalization for autonomous vehicle |
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Institutional Knowledge at Singapore Management University |
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2022 |
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https://ink.library.smu.edu.sg/cclaw/6 https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=1005&context=cclaw |
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