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

Lihua ZHAO
Ryutaro ICHISE
Zheng LIU
Seiichi MITA

IEICE TRANSACTIONS on Information and Systems   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
ontology,  knowledge representation,  knowledge base,  SPARQL,  C-SPARQL,  autonomous vehicles,  decision making systems,  Advanced Driver Assistance System (ADAS),  

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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.