Traffic Rules Encoding Using Defeasible Deontic Logic
Hanif Bhuiyan, Guido Governatori, Andy Bond, Sébastien Demmel, Mohammad Badiul Islam, Andry Rakotonirainy · Frontiers in artificial intelligence and applications · 2020
Automatically assessing driving behaviour against traffic rules is a challenging task for improving the safety of Automated Vehicles (AVs). There are no AV specific traffic rules against which AV behaviour can be assessed. Moreover current traffic rules can be imprecisely expressed and are sometimes conflicting making it hard to validate AV driving behaviour. Therefore, in this paper, we propose a Defeasible Deontic Logic (DDL) based driving behaviour assessment methodology for AVs. DDL is used to effectively handle rule exceptions and resolve conflicts in rule norms. A data-driven experiment is conducted to prove the effectiveness of the proposed methodology.