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A Rule-Based Behaviour Planner for Autonomous Driving

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Rules and Reasoning (RuleML+RR 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13752))

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Abstract

Autonomous vehicles require highly sophisticated decision-making to determine their motion. This paper describes how such functionality can be achieved with a practical rule engine learned from expert driving decisions. We propose an algorithm to create and maintain a rule-based behaviour planner, using a two-layer rule-based theory. The first layer determines a set of feasible parametrized behaviours, given the perceived state of the environment. From these, a resolution function chooses the most conservative high-level maneuver. The second layer then reconciles the parameters into a single behaviour. To demonstrate the practicality of our approach, we report results of its implementation in a level-3 autonomous vehicle and its field test in an urban environment.

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Notes

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References

  1. Aksjonov, A., Kyrki, V.: Rule-based decision-making system for autonomous vehicles at intersections with mixed traffic environment. In: International Intelligent Transportation Systems Conference (ITSC), pp. 660–666. IEEE (2021)

    Google Scholar 

  2. Bansal, M., Krizhevsky, A., Ogale, A.S.: ChauffeurNet: learning to drive by imitating the best and synthesizing the worst. In: Robotics: Science and Systems XV. RSS Foundation (2019)

    Google Scholar 

  3. Bojarski, M., Del Testa, D., Dworakowski, D., Firner, B., Flepp, B., Goyal, P., et al.: End to end learning for self-driving cars. arXiv: 1604.07316 (2016)

  4. Fu, Y., Li, C., Yu, F.R., Luan, T.H., Zhang, Y.: Hybrid autonomous driving guidance strategy combining deep reinforcement learning and expert system. Trans. Intell. Transp. Syst. (T-ITS) 23(8), 11273–11286 (2022)

    Google Scholar 

  5. Fürnkranz, J., Gamberger, D., Lavrač, N.: Foundations of Rule Learning. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-540-75197-7

    Book  MATH  Google Scholar 

  6. Fürnkranz, J., Hüllermeier, E., Mencía, E.L., Rapp, M.: Learning structured declarative rule sets - a challenge for deep discrete learning. arXiv: 2012.04377 (2020)

  7. González, D., Pérez, J., Milanés, V., Nashashibi, F.: A review of motion planning techniques for automated vehicles. Trans. Intell. Transp. Syst. (T-ITS) 17(4), 1135–1145 (2016)

    Google Scholar 

  8. Kapania, N.R., Govindarajan, V., Borrelli, F., Gerdes, J.C.: A hybrid control design for autonomous vehicles at uncontrolled crosswalks. In: Intelligent Vehicles Symposium (IV), pp. 1604–1611. IEEE (2019)

    Google Scholar 

  9. Kuderer, M., Gulati, S., Burgard, W.: Learning driving styles for autonomous vehicles from demonstration. In: International Conference on Robotics and Automation (ICRA), pp. 2641–2646. IEEE (2015)

    Google Scholar 

  10. Lee, J., Balakrishnan, A., Gaurav, A., Czarnecki, K., Sedwards, S.: WiseMove: a framework to investigate safe deep reinforcement learning for autonomous driving. In: Parker, D., Wolf, V. (eds.) QEST 2019. LNCS, vol. 11785, pp. 350–354. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30281-8_20

    Chapter  Google Scholar 

  11. Likmeta, A., Metelli, A.M., Tirinzoni, A., Giol, R., Restelli, M., Romano, D.: Combining reinforcement learning with rule-based controllers for transparent and general decision-making in autonomous driving. Robot. Auton. Syst. 131, 103568 (2020)

    Article  Google Scholar 

  12. Lipton, Z.C.: The mythos of model interpretability. Queue 16(3), 31–57 (2018)

    Article  Google Scholar 

  13. Montemerlo, M., Becker, J., Bhat, S., Dahlkamp, H., et al.: Junior: the Stanford entry in the urban challenge. J. Field Robot. 25(9), 569–597 (2008)

    Article  Google Scholar 

  14. Niehaus, A., Stengel, R.F.: An expert system for automated highway driving. IEEE Control Syst. Mag. 11(3), 53–61 (1991)

    Article  Google Scholar 

  15. Paden, B., Čáp, M., Yong, S.Z., Yershov, D., Frazzoli, E.: A survey of motion planning and control techniques for self-driving urban vehicles. Trans. Intell. Veh. (T-IV) 1(1), 33–55 (2016)

    Google Scholar 

  16. de Sainte Marie, C.: Learning decision rules or learning decision models? In: Moschoyiannis, S., Peñaloza, R., Vanthienen, J., Soylu, A., Roman, D. (eds.) RuleML+RR 2021. LNCS, vol. 12851, pp. 276–283. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-91167-6_19

    Chapter  Google Scholar 

  17. Shekhar, C., Moisan, S., Thonnat, M.: Use of a real-time perception program supervisor in a driving scenario. In: Intelligent Vehicles Symposium (IV), pp. 363–368. IEEE (1994)

    Google Scholar 

  18. Urmson, C., et al.: Autonomous driving in traffic: boss and the urban challenge. AI Mag. 30(2), 17 (2009)

    Google Scholar 

  19. Vanderhaegen, F.: A rule-based support system for dissonance discovery and control applied to car driving. Expert Syst. Appl. 65, 361–371 (2016)

    Article  Google Scholar 

  20. Wang, Q., Ayalew, B., Weiskircher, T.: Predictive maneuver planning for an autonomous vehicle in public highway traffic. Trans. Intell. Transp. Syst. 20(4), 1303–1315 (2019)

    Article  Google Scholar 

  21. Xiao, W., et al.: Rule-based optimal control for autonomous driving. In: ACM/IEEE 12th International Conference on Cyber-Physical Systems (ICCPS), pp. 143–154. ACM (2021)

    Google Scholar 

  22. Yay, E., Madrid, N.M., Ramírez, J.A.O.: Using an improved rule match algorithm in an expert system to detect broken driving rules for an energy-efficiency and safety relevant driving system. Procedia Comput. Sci. 35, 127–136 (2014)

    Article  Google Scholar 

  23. Zakas, N.C.: Understanding ECMAScript 6: The Definitive Guide for JavaScript Developers. No Starch Press, San Francisco (2016)

    Google Scholar 

  24. Zhao, L., Ichise, R., Sasaki, Y., Liu, Z., Yoshikawa, T.: Fast decision making using ontology-based knowledge base. In: Intelligent Vehicles Symposium (IV), pp. 173–178. IEEE (2016)

    Google Scholar 

  25. Zimmerman, N., Schlenoff, C., Balakirsky, S.: Implementing a rule-based system to represent decision criteria for on-road autonomous navigation. In: Spring Symposium on Knowledge Representation and Ontologies for Autonomous Systems. AAAI (2004)

    Google Scholar 

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Acknowledgment

This work was supported throughout its development by Fonds de Recherche du Québec – Nature et Technologies (FRQNT) and Natural Sciences and Engineering Research Council (NSERC) Discovery Grant. Author SS was supported by Japanese Science and Technology agency (JST) ERATO project JPMJER1603: HASUO Metamathematics for Systems Design.

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Authors and Affiliations

  1. University of Waterloo, Waterloo, Canada

    Frédéric Bouchard, Sean Sedwards & Krzysztof Czarnecki

Authors
  1. Frédéric Bouchard
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  2. Sean Sedwards
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  3. Krzysztof Czarnecki
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Corresponding author

Correspondence to Frédéric Bouchard .

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Editors and Affiliations

  1. Singapore Management University, Singapore, Singapore

    Guido Governatori

  2. Dresden University of Technology, Dresden, Germany

    Anni-Yasmin Turhan

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Bouchard, F., Sedwards, S., Czarnecki, K. (2022). A Rule-Based Behaviour Planner for Autonomous Driving. In: Governatori, G., Turhan, AY. (eds) Rules and Reasoning. RuleML+RR 2022. Lecture Notes in Computer Science, vol 13752. Springer, Cham. https://doi.org/10.1007/978-3-031-21541-4_17

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  • DOI: https://doi.org/10.1007/978-3-031-21541-4_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21540-7

  • Online ISBN: 978-3-031-21541-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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