IEICE TRANSACTIONS on Information
Explanatory Rule Generation for Advanced Driver Assistant Systems
Summary :
Autonomous vehicles and advanced driver assistant systems (ADAS) are receiving notable attention as research fields in both academia and private industry. Some decision-making systems use sets of logical rules to map knowledge of the ego-vehicle and its environment into actions the ego-vehicle should take. However, such rulesets can be difficult to create — for example by manually writing them — due to the complexity of traffic as an operating environment. Furthermore, the building blocks of the rules must be defined. One common solution to this is using an ontology specifically aimed at describing traffic concepts and their hierarchy. These ontologies must have a certain expressive power to enable construction of useful rules. We propose a process of generating sets of explanatory rules for ADAS applications from data using ontology as a base vocabulary and present a ruleset generated as a result of our experiments that is correct for the scope of the experiment.
- Publication
- IEICE TRANSACTIONS on Information Vol.E104-D No.9 pp.1427-1439
- Publication Date
- 2021/09/01
- Publicized
- 2021/06/11
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2020EDP7206
- Type of Manuscript
- PAPER
- Category
- Artificial Intelligence, Data Mining
Authors
Juha HOVI
Graduate University for Advanced Studies SOKENDAI,National Institute of Informatics
Ryutaro ICHISE
National Institute of Informatics,Graduate University for Advanced Studies SOKENDAI
Keyword
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Juha HOVI, Ryutaro ICHISE, "Explanatory Rule Generation for Advanced Driver Assistant Systems" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 9, pp. 1427-1439, September 2021, doi: 10.1587/transinf.2020EDP7206.
Abstract: Autonomous vehicles and advanced driver assistant systems (ADAS) are receiving notable attention as research fields in both academia and private industry. Some decision-making systems use sets of logical rules to map knowledge of the ego-vehicle and its environment into actions the ego-vehicle should take. However, such rulesets can be difficult to create — for example by manually writing them — due to the complexity of traffic as an operating environment. Furthermore, the building blocks of the rules must be defined. One common solution to this is using an ontology specifically aimed at describing traffic concepts and their hierarchy. These ontologies must have a certain expressive power to enable construction of useful rules. We propose a process of generating sets of explanatory rules for ADAS applications from data using ontology as a base vocabulary and present a ruleset generated as a result of our experiments that is correct for the scope of the experiment.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020EDP7206/_p
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@ARTICLE{e104-d_9_1427,
author={Juha HOVI, Ryutaro ICHISE, },
journal={IEICE TRANSACTIONS on Information},
title={Explanatory Rule Generation for Advanced Driver Assistant Systems},
year={2021},
volume={E104-D},
number={9},
pages={1427-1439},
abstract={Autonomous vehicles and advanced driver assistant systems (ADAS) are receiving notable attention as research fields in both academia and private industry. Some decision-making systems use sets of logical rules to map knowledge of the ego-vehicle and its environment into actions the ego-vehicle should take. However, such rulesets can be difficult to create — for example by manually writing them — due to the complexity of traffic as an operating environment. Furthermore, the building blocks of the rules must be defined. One common solution to this is using an ontology specifically aimed at describing traffic concepts and their hierarchy. These ontologies must have a certain expressive power to enable construction of useful rules. We propose a process of generating sets of explanatory rules for ADAS applications from data using ontology as a base vocabulary and present a ruleset generated as a result of our experiments that is correct for the scope of the experiment.},
keywords={},
doi={10.1587/transinf.2020EDP7206},
ISSN={1745-1361},
month={September},}
Copy
TY - JOUR
TI - Explanatory Rule Generation for Advanced Driver Assistant Systems
T2 - IEICE TRANSACTIONS on Information
SP - 1427
EP - 1439
AU - Juha HOVI
AU - Ryutaro ICHISE
PY - 2021
DO - 10.1587/transinf.2020EDP7206
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 9
JA - IEICE TRANSACTIONS on Information
Y1 - September 2021
AB - Autonomous vehicles and advanced driver assistant systems (ADAS) are receiving notable attention as research fields in both academia and private industry. Some decision-making systems use sets of logical rules to map knowledge of the ego-vehicle and its environment into actions the ego-vehicle should take. However, such rulesets can be difficult to create — for example by manually writing them — due to the complexity of traffic as an operating environment. Furthermore, the building blocks of the rules must be defined. One common solution to this is using an ontology specifically aimed at describing traffic concepts and their hierarchy. These ontologies must have a certain expressive power to enable construction of useful rules. We propose a process of generating sets of explanatory rules for ADAS applications from data using ontology as a base vocabulary and present a ruleset generated as a result of our experiments that is correct for the scope of the experiment.
ER -
English
