Improving Driver Assistance in Intelligent Transportation Systems: An Agent-Based Evidential Reasoning Approach
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...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2020/4607858 |
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| _version_ | 1832566084836786176 |
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| author | M. Benalla B. Achchab H. Hrimech |
| author_facet | M. Benalla B. Achchab H. Hrimech |
| author_sort | M. Benalla |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-95a827de71c24069b0c77d4ec3ee1f5e |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-95a827de71c24069b0c77d4ec3ee1f5e2025-02-03T01:05:16ZengWileyJournal of Advanced Transportation0197-67292042-31952020-01-01202010.1155/2020/46078584607858Improving Driver Assistance in Intelligent Transportation Systems: An Agent-Based Evidential Reasoning ApproachM. Benalla0B. Achchab1H. Hrimech2Laboratoire Analyse et Modélisation des Systèmes et Aide à la Décision ENSA de Berrechid, Université Hassan 1er de Settat, Settat, MoroccoLaboratoire Analyse et Modélisation des Systèmes et Aide à la Décision ENSA de Berrechid, Université Hassan 1er de Settat, Settat, MoroccoLaboratoire Analyse et Modélisation des Systèmes et Aide à la Décision ENSA de Berrechid, Université Hassan 1er de Settat, Settat, MoroccoProviding 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.http://dx.doi.org/10.1155/2020/4607858 |
| spellingShingle | M. Benalla B. Achchab H. Hrimech Improving Driver Assistance in Intelligent Transportation Systems: An Agent-Based Evidential Reasoning Approach Journal of Advanced Transportation |
| title | Improving Driver Assistance in Intelligent Transportation Systems: An Agent-Based Evidential Reasoning Approach |
| title_full | Improving Driver Assistance in Intelligent Transportation Systems: An Agent-Based Evidential Reasoning Approach |
| title_fullStr | Improving Driver Assistance in Intelligent Transportation Systems: An Agent-Based Evidential Reasoning Approach |
| title_full_unstemmed | Improving Driver Assistance in Intelligent Transportation Systems: An Agent-Based Evidential Reasoning Approach |
| title_short | Improving Driver Assistance in Intelligent Transportation Systems: An Agent-Based Evidential Reasoning Approach |
| title_sort | improving driver assistance in intelligent transportation systems an agent based evidential reasoning approach |
| url | http://dx.doi.org/10.1155/2020/4607858 |
| work_keys_str_mv | AT mbenalla improvingdriverassistanceinintelligenttransportationsystemsanagentbasedevidentialreasoningapproach AT bachchab improvingdriverassistanceinintelligenttransportationsystemsanagentbasedevidentialreasoningapproach AT hhrimech improvingdriverassistanceinintelligenttransportationsystemsanagentbasedevidentialreasoningapproach |