Ontology-Based Driving Decision Making: A Feasibility Study at Uncontrolled Intersections

Lihua ZHAO; Ryutaro ICHISE; Zheng LIU; Seiichi MITA; Yutaka SASAKI

doi:10.1587/transinf.2016EDP7337

Ontology-Based Driving Decision Making: A Feasibility Study at Uncontrolled Intersections

Lihua ZHAO, Ryutaro ICHISE, Zheng LIU, Seiichi MITA, Yutaka SASAKI

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This paper presents an ontology-based driving decision making system, which can promptly make safety decisions in real-world driving. Analyzing sensor data for improving autonomous driving safety has become one of the most promising issues in the autonomous vehicles research field. However, representing the sensor data in a machine understandable format for further knowledge processing still remains a challenging problem. In this paper, we introduce ontologies designed for autonomous vehicles and ontology-based knowledge base, which are used for representing knowledge of maps, driving paths, and perceived driving environments. Advanced Driver Assistance Systems (ADAS) are developed to improve safety of autonomous vehicles by accessing to the ontology-based knowledge base. The ontologies can be reused and extended for constructing knowledge base for autonomous vehicles as well as for implementing different types of ADAS such as decision making system.

Publication: IEICE TRANSACTIONS on Information Vol.E100-D No.7 pp.1425-1439

Publication Date: 2017/07/01

Publicized: 2017/04/05

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2016EDP7337

Type of Manuscript: PAPER

Category: Artificial Intelligence, Data Mining

Authors

Lihua ZHAO
  National Institute of Advanced Industrial Science and Technology (AIST)
Ryutaro ICHISE
  National Institute of Informatics (NII)
Zheng LIU
  University of British Columbia
Seiichi MITA
  Toyota Technological Institute (TTI)
Yutaka SASAKI
  Toyota Technological Institute (TTI)

Keyword

ontology, knowledge representation, knowledge base, SPARQL, C-SPARQL, autonomous vehicles, decision making systems, Advanced Driver Assistance System (ADAS)

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Lihua ZHAO, Ryutaro ICHISE, Zheng LIU, Seiichi MITA, Yutaka SASAKI, "Ontology-Based Driving Decision Making: A Feasibility Study at Uncontrolled Intersections" in IEICE TRANSACTIONS on Information, vol. E100-D, no. 7, pp. 1425-1439, July 2017, doi: 10.1587/transinf.2016EDP7337.
Abstract: This paper presents an ontology-based driving decision making system, which can promptly make safety decisions in real-world driving. Analyzing sensor data for improving autonomous driving safety has become one of the most promising issues in the autonomous vehicles research field. However, representing the sensor data in a machine understandable format for further knowledge processing still remains a challenging problem. In this paper, we introduce ontologies designed for autonomous vehicles and ontology-based knowledge base, which are used for representing knowledge of maps, driving paths, and perceived driving environments. Advanced Driver Assistance Systems (ADAS) are developed to improve safety of autonomous vehicles by accessing to the ontology-based knowledge base. The ontologies can be reused and extended for constructing knowledge base for autonomous vehicles as well as for implementing different types of ADAS such as decision making system.
URL: https://globals.ieice.org/en_transactions/information/10.1587/transinf.2016EDP7337/_p

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@ARTICLE{e100-d_7_1425,
author={Lihua ZHAO, Ryutaro ICHISE, Zheng LIU, Seiichi MITA, Yutaka SASAKI, },
journal={IEICE TRANSACTIONS on Information},
title={Ontology-Based Driving Decision Making: A Feasibility Study at Uncontrolled Intersections},
year={2017},
volume={E100-D},
number={7},
pages={1425-1439},
abstract={This paper presents an ontology-based driving decision making system, which can promptly make safety decisions in real-world driving. Analyzing sensor data for improving autonomous driving safety has become one of the most promising issues in the autonomous vehicles research field. However, representing the sensor data in a machine understandable format for further knowledge processing still remains a challenging problem. In this paper, we introduce ontologies designed for autonomous vehicles and ontology-based knowledge base, which are used for representing knowledge of maps, driving paths, and perceived driving environments. Advanced Driver Assistance Systems (ADAS) are developed to improve safety of autonomous vehicles by accessing to the ontology-based knowledge base. The ontologies can be reused and extended for constructing knowledge base for autonomous vehicles as well as for implementing different types of ADAS such as decision making system.},
keywords={},
doi={10.1587/transinf.2016EDP7337},
ISSN={1745-1361},
month={July},}

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TY - JOUR
TI - Ontology-Based Driving Decision Making: A Feasibility Study at Uncontrolled Intersections
T2 - IEICE TRANSACTIONS on Information
SP - 1425
EP - 1439
AU - Lihua ZHAO
AU - Ryutaro ICHISE
AU - Zheng LIU
AU - Seiichi MITA
AU - Yutaka SASAKI
PY - 2017
DO - 10.1587/transinf.2016EDP7337
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2017
AB - This paper presents an ontology-based driving decision making system, which can promptly make safety decisions in real-world driving. Analyzing sensor data for improving autonomous driving safety has become one of the most promising issues in the autonomous vehicles research field. However, representing the sensor data in a machine understandable format for further knowledge processing still remains a challenging problem. In this paper, we introduce ontologies designed for autonomous vehicles and ontology-based knowledge base, which are used for representing knowledge of maps, driving paths, and perceived driving environments. Advanced Driver Assistance Systems (ADAS) are developed to improve safety of autonomous vehicles by accessing to the ontology-based knowledge base. The ontologies can be reused and extended for constructing knowledge base for autonomous vehicles as well as for implementing different types of ADAS such as decision making system.
ER -