The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Kenderaan autonomi dan sistem pembantu pemandu lanjutan (ADAS) menerima perhatian yang ketara sebagai bidang penyelidikan dalam kedua-dua industri akademik dan swasta. Sesetengah sistem membuat keputusan menggunakan set peraturan logik untuk memetakan pengetahuan tentang kenderaan ego dan persekitarannya ke dalam tindakan yang harus dilakukan oleh kenderaan ego. Walau bagaimanapun, set peraturan sedemikian mungkin sukar dibuat — contohnya dengan menulisnya secara manual — disebabkan oleh kerumitan trafik sebagai persekitaran operasi. Tambahan pula, blok bangunan peraturan mesti ditakrifkan. Satu penyelesaian biasa untuk perkara ini ialah menggunakan ontologi yang khusus bertujuan untuk menerangkan konsep lalu lintas dan hierarkinya. Ontologi ini mesti mempunyai kuasa ekspresif tertentu untuk membolehkan pembinaan peraturan yang berguna. Kami mencadangkan satu proses menjana set peraturan penerangan untuk aplikasi ADAS daripada data menggunakan ontologi sebagai perbendaharaan kata asas dan membentangkan set peraturan yang dijana hasil daripada eksperimen kami yang betul untuk skop percubaan.
Juha HOVI
Graduate University for Advanced Studies SOKENDAI,National Institute of Informatics
Ryutaro ICHISE
National Institute of Informatics,Graduate University for Advanced Studies SOKENDAI
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Salinan
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
Salinan
@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},}
Salinan
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 -