Driving Decision Making of Autonomous Vehicle According to Queensland Overtaking Traffic Rules

Hanif Bhuiyan (), Guido Governatori (), Andry Rakotonirainy (), Meng Weng Wong () and Avishkar Mahajan ()
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Hanif Bhuiyan: Monash University, Monash Data Futures Institute
Guido Governatori: Singapore Management University
Andry Rakotonirainy: Queensland University of Technology, CARRS-Q
Meng Weng Wong: Singapore Management University
Avishkar Mahajan: Singapore Management University

The Review of Socionetwork Strategies, 2023, vol. 17, issue 2, 233-254

Abstract: Abstract Improving the safety of autonomous vehicles (AVs) by making driving decisions in accordance with traffic rules is a complex task. Traffic rules are often expressed in a way that allows for interpretation and exceptions, making it difficult for AVs to follow them. This paper proposes a novel methodology for driving decision making in AVs based on defeasible deontic logic (DDL). We use DDL to formalize traffic rules and facilitate automated reasoning, allowing for the effective handling of rule exceptions and the resolution of vague terms in rules. To supplement the information provided by traffic rules, we incorporate an ontology for AV driving behaviour and environment information. By applying automated reasoning to formalized traffic rules and ontology-based AV driving information, our methodology enables AVs to make driving decisions in accordance with traffic rules. We present a case study focussing on the overtaking traffic rule to illustrate the usefulness of our methodology. Our evaluation demonstrates the effectiveness of the proposed driving decision-making methodology, highlighting its potential to improve the safety of AVs on the road.

Keywords: Autonomous vehicle; Driving decision; Overtaking; Defeasible deontic logic (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s12626-023-00147-x

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