Relating Driver Behaviour and Response to Messages through HMI in Autonomous and Connected Vehicular Environment
Mental, physical, and emotional workload is the main source for the driver’s stress, fatigue, and distraction, which affects the driving tasks performed by the driver in terms of responding to the surroundings and the delay in reacting to events. Driver behaviour is an uncertain element within Intel...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2022-12-01
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| Series: | Cogent Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/23311916.2021.2002793 |
| _version_ | 1797762670090256384 |
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| author | Mahmoud Zaki Iskandarani |
| author_facet | Mahmoud Zaki Iskandarani |
| author_sort | Mahmoud Zaki Iskandarani |
| collection | DOAJ |
| description | Mental, physical, and emotional workload is the main source for the driver’s stress, fatigue, and distraction, which affects the driving tasks performed by the driver in terms of responding to the surroundings and the delay in reacting to events. Driver behaviour is an uncertain element within Intelligent Transportation Systems (ITS). Thus, this work presents a mathematical model that accounts for three main elements in ADAS-assisted vehicles, autonomous vehicles, connected vehicles, and autonomous and connected vehicles. The model accounts for processing times by the On-Board Unit (OBU), driver time, which covers both perception and response times, and communication time, which covers exchanged messages between vehicles and/ or infrastructure. The model shows two distinct behaviours, power law trend in the case of ADAS and exponential trend in the case of autonomous and connected vehicles, with both power and exponential interacting in the case of connected vehicles. In all considered scenarios, the mathematical model presents a dynamically interacting usability function that contains probability, Gaussian interpolation, and value limiting functions. The work also shows the effect of each time component on the usability, and hence the effectiveness of avoiding incidents. The model helps in optimizing both HMI, electronics, and communication designs. Driver time, in the case of ADAS, connected vehicles, and under rare conditions in autonomous and connected vehicles is critical, hence HMI optimization is important even though in autonomous and connected vehicles is not always needed. The work shows that the best driving usability is in the case of autonomous and connected vehicles. |
| first_indexed | 2024-03-12T19:31:43Z |
| format | Article |
| id | doaj.art-a32272004c0648f7a93a40faf95896d4 |
| institution | Directory Open Access Journal |
| issn | 2331-1916 |
| language | English |
| last_indexed | 2024-03-12T19:31:43Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Engineering |
| spelling | doaj.art-a32272004c0648f7a93a40faf95896d42023-08-02T04:31:36ZengTaylor & Francis GroupCogent Engineering2331-19162022-12-019110.1080/23311916.2021.20027932002793Relating Driver Behaviour and Response to Messages through HMI in Autonomous and Connected Vehicular EnvironmentMahmoud Zaki Iskandarani0Al-Ahliyya Amman UniversityMental, physical, and emotional workload is the main source for the driver’s stress, fatigue, and distraction, which affects the driving tasks performed by the driver in terms of responding to the surroundings and the delay in reacting to events. Driver behaviour is an uncertain element within Intelligent Transportation Systems (ITS). Thus, this work presents a mathematical model that accounts for three main elements in ADAS-assisted vehicles, autonomous vehicles, connected vehicles, and autonomous and connected vehicles. The model accounts for processing times by the On-Board Unit (OBU), driver time, which covers both perception and response times, and communication time, which covers exchanged messages between vehicles and/ or infrastructure. The model shows two distinct behaviours, power law trend in the case of ADAS and exponential trend in the case of autonomous and connected vehicles, with both power and exponential interacting in the case of connected vehicles. In all considered scenarios, the mathematical model presents a dynamically interacting usability function that contains probability, Gaussian interpolation, and value limiting functions. The work also shows the effect of each time component on the usability, and hence the effectiveness of avoiding incidents. The model helps in optimizing both HMI, electronics, and communication designs. Driver time, in the case of ADAS, connected vehicles, and under rare conditions in autonomous and connected vehicles is critical, hence HMI optimization is important even though in autonomous and connected vehicles is not always needed. The work shows that the best driving usability is in the case of autonomous and connected vehicles.http://dx.doi.org/10.1080/23311916.2021.2002793driver behaviourhmiprobabilityusabilityautonomous vehiclesconnected vehiclesgaussian interpolation |
| spellingShingle | Mahmoud Zaki Iskandarani Relating Driver Behaviour and Response to Messages through HMI in Autonomous and Connected Vehicular Environment Cogent Engineering driver behaviour hmi probability usability autonomous vehicles connected vehicles gaussian interpolation |
| title | Relating Driver Behaviour and Response to Messages through HMI in Autonomous and Connected Vehicular Environment |
| title_full | Relating Driver Behaviour and Response to Messages through HMI in Autonomous and Connected Vehicular Environment |
| title_fullStr | Relating Driver Behaviour and Response to Messages through HMI in Autonomous and Connected Vehicular Environment |
| title_full_unstemmed | Relating Driver Behaviour and Response to Messages through HMI in Autonomous and Connected Vehicular Environment |
| title_short | Relating Driver Behaviour and Response to Messages through HMI in Autonomous and Connected Vehicular Environment |
| title_sort | relating driver behaviour and response to messages through hmi in autonomous and connected vehicular environment |
| topic | driver behaviour hmi probability usability autonomous vehicles connected vehicles gaussian interpolation |
| url | http://dx.doi.org/10.1080/23311916.2021.2002793 |
| work_keys_str_mv | AT mahmoudzakiiskandarani relatingdriverbehaviourandresponsetomessagesthroughhmiinautonomousandconnectedvehicularenvironment |