Latest revision as of 00:32, 23 March 2021

Abstract

Providing accurate real-time traffic information is an inherent problem for intelligent transportation systems (ITS). In order to improve the knowledge base of advanced driver assistance systems (ADAS), ITS are strongly concerned with data fusion techniques of all kinds of sensors deployed over the traffic network. Driver assistance is devoid of a comprehensive evidential reasoning system on contextual information, more specifically when a combination involves inside and outside sensory information of the driving environment. In this paper, we propose a novel agent-based evidential reasoning system using contextual information. Based on a series of information handling techniques, specifically, the belief functions theory and heuristic inference operations to achieve a consensus about daily driving activity in automatically inferring. That is quite different from other existing proposals, as it deals jointly with the driving behavior and the driving environment conditions. A case study including various scenarios of experiments is introduced to estimate behavioral information based on synthetic data for prediction, prescription, and policy analysis. Our experiments show promising, thought-provoking results encouraging further research.

Document type: Article

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The different versions of the original document can be found in:

• http://downloads.hindawi.com/journals/jat/2020/4607858.pdf under the license https://creativecommons.org/licenses/by

• http://dx.doi.org/10.1155/2020/4607858 under the license cc-by

• http://downloads.hindawi.com/journals/jat/2020/4607858.pdf,

http://downloads.hindawi.com/journals/jat/2020/4607858.xml,

http://dx.doi.org/10.1155/2020/4607858 under the license http://creativecommons.org/licenses/by/4.0

• http://dx.doi.org/10.1155/2020/4607858,

https://doaj.org/toc/0197-6729,

https://doaj.org/toc/2042-3195 under the license http://creativecommons.org/licenses/by/4.0/

• https://www.hindawi.com/journals/jat/2020/4607858,

http://downloads.hindawi.com/journals/jat/2020/4607858.pdf,

https://academic.microsoft.com/#/detail/2996913003