Paper Info
Title
Explanatory Rule Generation for Advanced Driver Assistant Systems
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.
Year
DOI
Venue
2021
10.1587/transinf.2020EDP7206
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
Keywords
DocType
Volume
advanced driver assistant system, ADAS, ontology, rule-based reasoning, decision-making, knowledge representation, machine learning
Journal
E104D
Issue
ISSN
Citations 
9
1745-1361
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Juha Hovi100.34
Ryutaro Ichise200.34