Authors:
Tobias Meuser
;
Martin Wende
;
Patrick Lieser
;
Björn Richerzhagen
and
Ralf Steinmetz
Affiliation:
Technische Universität Darmstadt, Germany
Keyword(s):
Decision-making, Distributed, Quality of Information, Information Lifetime, Information Accuracy.
Related
Ontology
Subjects/Areas/Topics:
Applications and Uses
;
Artificial Intelligence
;
Business Analytics
;
Cardiovascular Technologies
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Health Engineering and Technology Applications
;
Knowledge-Based Systems
;
Sensor Networks
;
Sensor, Mesh and Ad Hoc Communications and Networks
;
Symbolic Systems
;
Telecommunications
;
Vehicular Networks
;
Wireless Information Networks and Systems
Abstract:
To increase road safety and efficiency, connected vehicles rely on the exchange of information. On each
vehicle, a decision-making algorithm processes the received information and determines the actions that are to
be taken. State-of-the-art decision approaches focus on static information and ignore the temporal dynamics
of the environment, which is characterized by high change rates in a vehicular scenario. Hence, they keep
outdated information longer than necessary and miss optimization potential. To address this problem, we
propose a quality of information (QoI) weight based on a Hidden Markov Model for each information type,
e.g., a road congestion state. Using this weight in the decision process allows us to combine detection accuracy
of the sensor and the information lifetime in the decision-making, and, consequently, adapt to environmental
changes significantly faster. We evaluate our approach for the scenario of traffic jam detection and avoidance,
showing that it re
duces the costs of false decisions by up to 25% compared to existing approaches.
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