A validated fuzzy logic inspired driver distraction evaluation system for road safety using artificial human driver emotion
This research paper presents a validated emotion enabled cognitive driver assistance model (EECDAM) as an accident prevention scheme while keeping in mind different types of driver distractions. It is observed that distracted drivers know that distraction can lead them to a crash but they are not aw...
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| Published in | Computer networks (Amsterdam, Netherlands : 1999) Vol. 143; pp. 62 - 73 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Amsterdam
Elsevier B.V
09.10.2018
Elsevier Sequoia S.A |
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| Abstract | This research paper presents a validated emotion enabled cognitive driver assistance model (EECDAM) as an accident prevention scheme while keeping in mind different types of driver distractions. It is observed that distracted drivers know that distraction can lead them to a crash but they are not aware of distractions when they take over and they continue to drive. With advancements in autonomous vehicles technologies, it is possible to have an onboard driver assistance systems. However, research is yet to be reported on this issue whether onboard driver assistance program will be effective or not. The Emotion Enabled Cognitive Driver Assistance Model is a system based on an encapsulated Emotion Enabled Cognitive Driver Assistant (EECDA), which computes the effects of external factors at the distraction level of the subject and generates algorithmically generated fear emotion. During experiments, the EECDA intervenes when the fear intensity of the driver crosses a threshold by sending two sound alerts to the driver to take appropriate action. To demonstrate the effectiveness of the proposed approach as a road safety system, a Cognitive Agent-Based Computing (CABC) framework has been utilized to validate the results of the EECDAM. Algorithms are utilized using fuzzy sets to compute distraction of the drivers. We also present an Agent-Based Model (ABM) to validate the implementation of the proposed scheme. Extensive experiments demonstrate the proficiency of the proposed model for robust collision avoidance. |
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| AbstractList | This research paper presents a validated emotion enabled cognitive driver assistance model (EECDAM) as an accident prevention scheme while keeping in mind different types of driver distractions. It is observed that distracted drivers know that distraction can lead them to a crash but they are not aware of distractions when they take over and they continue to drive. With advancements in autonomous vehicles technologies, it is possible to have an onboard driver assistance systems. However, research is yet to be reported on this issue whether onboard driver assistance program will be effective or not. The Emotion Enabled Cognitive Driver Assistance Model is a system based on an encapsulated Emotion Enabled Cognitive Driver Assistant (EECDA), which computes the effects of external factors at the distraction level of the subject and generates algorithmically generated fear emotion. During experiments, the EECDA intervenes when the fear intensity of the driver crosses a threshold by sending two sound alerts to the driver to take appropriate action. To demonstrate the effectiveness of the proposed approach as a road safety system, a Cognitive Agent-Based Computing (CABC) framework has been utilized to validate the results of the EECDAM. Algorithms are utilized using fuzzy sets to compute distraction of the drivers. We also present an Agent-Based Model (ABM) to validate the implementation of the proposed scheme. Extensive experiments demonstrate the proficiency of the proposed model for robust collision avoidance. |
| Author | Ahmad, Mudassar Chaudhry, Junaid Jabbar, Sohail Khadim, Sania Rauf, Rabia Riaz, Faisal |
| Author_xml | – sequence: 1 givenname: Faisal surname: Riaz fullname: Riaz, Faisal organization: Department of Computer Science & IT, Mirpur University of Science and Technology, Pakistan – sequence: 2 givenname: Sania surname: Khadim fullname: Khadim, Sania email: sania.csit@mustedu.pk organization: Department of Computer Science & IT, Mirpur University of Science and Technology, Pakistan – sequence: 3 givenname: Rabia surname: Rauf fullname: Rauf, Rabia email: rabia.csit@must.edu.pk organization: Department of Computer Science & IT, Mirpur University of Science and Technology, Pakistan – sequence: 4 givenname: Mudassar orcidid: 0000-0002-6366-8230 surname: Ahmad fullname: Ahmad, Mudassar organization: Department of Computer Science, National Textile University, Faisalabad, Pakistan – sequence: 5 givenname: Sohail surname: Jabbar fullname: Jabbar, Sohail organization: Department of Computer Science, National Textile University, Faisalabad, Pakistan – sequence: 6 givenname: Junaid orcidid: 0000-0003-2000-7915 surname: Chaudhry fullname: Chaudhry, Junaid email: chaudhrj@erau.edu, chaudhry@ieee.org organization: Cyber Intelligence & Security Department, College of Security and Intelligence, Embry-Riddle Aeronautical University, Prescott, AZ, USA |
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| Keywords | Fuzzy logic Artificial emotions Driver assistant model Driver distraction learning Safe road traffic system |
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| Snippet | This research paper presents a validated emotion enabled cognitive driver assistance model (EECDAM) as an accident prevention scheme while keeping in mind... |
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| SubjectTerms | Accident prevention Algorithms Artificial emotions Automobile drivers Collision avoidance Driver assistant model Driver behavior Driver distraction learning Drivers Emotions Fear Forensic engineering Fuzzy logic Fuzzy sets Fuzzy systems Onboard equipment Safe road traffic system Safety Scientific papers Traffic accidents & safety Validation studies |
| Title | A validated fuzzy logic inspired driver distraction evaluation system for road safety using artificial human driver emotion |
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