Abstract
Ontology-based systems can offer enticing benefits for autonomous vehicle applications. One such system is an ontology-based decision-making system. This system takes advantage of highly abstracted semantic knowledge that describes the state of the vehicle as well as the state of its environment. Knowledge on scenario state combined with a set of logical rules is then used to determine correct actions for the vehicle. However, creating a set of rules for this safety-critical application is a challenging problem which must be solved to enable the use of the decision-making system in practical applications. This work explores the feasibility of generating rules for the reasoning system through machine learning. We propose a process for the rule generation and create a set of rules describing vehicle behavior in an uncontrolled four-way intersection.
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References
Agrawal, R., Imieliński, T., Swami, A.: Mining association rules between sets of items in large databases. SIGMOD Rec. 22(2), 207–216 (1993)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of 20th International Conference on Very Large Data Bases, pp. 487–499. Morgan Kaufmann (1994)
Bojarski, M., et al.: End to end learning for self-driving cars. arXiv:1604.07316v1 (2016)
Dosovitskiy, A., Ros, G., Codevilla, F., Lopez, A., Koltun, V.: CARLA: an open urban driving simulator. In: Proceedings of the 1st Annual Conference on Robot Learning, pp. 1–16 (2017)
Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the KITTI dataset. Int. J. Robot. Res. 32(11), 1231–1237 (2013)
Horrocks, I., Patel-Schneider, P.F., Boley, H., Tabet, S., Grosofand, B., Dean, M.: SWRL: a semantic web rule language combining OWL and RuleML. W3C Member Submission (2004)
Ichise Laboratory: Advanced driver assistant system ontology. http://ri-www.nii.ac.jp/ADAS/index.html
Zhao, L., Ichise, R., Liu, Z., Mita, S., Sasaki, Y.: Ontology-based driving decision making: a feasibility study at uncontrolled intersections. IEICE Trans. Inf. Syst. 100, 1425–1439 (2017)
Zhao, L., Ichise, R., Mita, S., Sasaki, Y.: Core ontologies for safe autonomous driving. In: Proceedings of the ISWC Poster and Demonstrations Track (2015)
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Hovi, J., Ichise, R. (2020). Feasibility Study: Rule Generation for Ontology-Based Decision-Making Systems. In: Wang, X., Lisi, F., Xiao, G., Botoeva, E. (eds) Semantic Technology. JIST 2019. Communications in Computer and Information Science, vol 1157. Springer, Singapore. https://doi.org/10.1007/978-981-15-3412-6_9
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DOI: https://doi.org/10.1007/978-981-15-3412-6_9
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