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Traffic rules compliance checking of automated vehicle maneuvers

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Abstract

Automated Vehicles (AVs) are designed and programmed to follow traffic rules. However, there is no separate and comprehensive regulatory framework dedicated to AVs. The current Queensland traffic rules were designed for humans. These rules often contain open texture expressions, exceptions, and potential conflicts (conflict arises when exceptions cannot be handled in rules), which makes it hard for AVs to follow. This paper presents an automatic compliance checking framework to assess AVs behaviour against current traffic rules by addressing these issues. Specifically, it proposes a framework to determine which traffic rules and open texture expressions need some additional interpretation. Essentially this enables AVs to have a suitable and executable formalization of the traffic rules. Defeasible Deontic Logic (DDL) is used to formalize traffic rules and reasoning with AV information (behaviour and environment). The representation of rules in DDL helps effectively in handling and resolving exceptions, potential conflicts, and open textures in rules. 40 experiments were conducted on eight realistic traffic scenarios to evaluate the framework. The evaluation was undertaken both quantitatively and qualitatively. The evaluation result shows that the proposed framework is a promising system for checking Automated Vehicle interpretation and compliance with current traffic rules.

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Notes

  1. http://www.businessinsider.com/companies-making-driverless-cars-by-2020-2016-8

  2. These patterns are sufficient to cover the majority of cases of QLD overtaking traffic rules. While more patterns are possible, the patterns we present also offer guidance to capture more complex cases if needed.

  3. https://protege.stanford.edu/

  4. https://www.tmr.qld.gov.au/Travel-and-transport/Maps-and-guides

  5. http://www.w3.org/TR/rdf-sparql-query/

  6. https://turnipbox.netlify.com/

  7. https://research.qut.edu.au/carrsq/engage/research-infrastructure/

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Acknowledgements

Special thanks to all authors for their contribution.

Funding

This research is funded by Data61, CSIRO and CARRS-Q, QUT.

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Authors and Affiliations

  1. Data61, CSIRO, Brisbane, Australia

    Hanif Bhuiyan

  2. Centre for Accident Research and Road Safety (CARRS-Q), Queensland University of Technology (QUT), 130 Victoria Park Road, Kelvin Grove, Brisbane, QLD, Australia

    Hanif Bhuiyan, Andy Bond & Andry Rakotonirainy

  3. Brisbane, Australia

    Guido Governatori

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Contributions

HB: Conceptualization, Investigation, Data Curation, Methodology, Evaluation, Software Development, Writing- Original draft. GG: Supervision, Conceptualization, Methodology, Writing-Reviewing and Editing. AB: Supervision, Writing-Reviewing and Editing. AR: Supervision, Writing-Reviewing and Editing.

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Correspondence to Hanif Bhuiyan.

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The QUT Human Research Ethics Committee approved the ethical clearance (Approval No.: 2021000109) for human participation in this research.

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Appendix: Formalization of QLD overtaking traffic rule

Appendix: Formalization of QLD overtaking traffic rule

See Tables 11, 12, 13, 14, 15, 16, 17, 18.

Table 11 Formalization of QLD Traffic Rule 140
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Table 12 Formalization of QLD Traffic Rule 141
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Table 13 Formalization of QLD Traffic Rule 142
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Table 14 Formalization of QLD Traffic Rule 143 (left overtaking)
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Table 15 Formalization of QLD Traffic Rule 143 (right overtaking)
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Table 16 Formalization of QLD Traffic Rule 144
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Table 17 Formalization of QLD Traffic Rule 144A
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Table 18 Formalization of QLD Traffic Rule 145
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Bhuiyan, H., Governatori, G., Bond, A. et al. Traffic rules compliance checking of automated vehicle maneuvers. Artif Intell Law 32, 1–56 (2024). https://doi.org/10.1007/s10506-022-09340-9

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