Ensuring Driving and Road Safety of Autonomous Vehicles Using a Control Optimiser Interaction Framework Through Smart “Thing” Information Sensing and Actuation
Road safety through point-to-point interaction autonomous vehicles (AVs) assimilate different communication technologies for reliable and persistent information sharing. Vehicle interaction resilience and consistency require novel sharing knowledge for retaining driving and pedestrian safety. This a...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/12/11/798 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846153054105108480 |
|---|---|
| author | Ahmed Almutairi Abdullah Faiz Al Asmari Tariq Alqubaysi Fayez Alanazi Ammar Armghan |
| author_facet | Ahmed Almutairi Abdullah Faiz Al Asmari Tariq Alqubaysi Fayez Alanazi Ammar Armghan |
| author_sort | Ahmed Almutairi |
| collection | DOAJ |
| description | Road safety through point-to-point interaction autonomous vehicles (AVs) assimilate different communication technologies for reliable and persistent information sharing. Vehicle interaction resilience and consistency require novel sharing knowledge for retaining driving and pedestrian safety. This article proposes a control optimiser interaction framework (COIF) for organising information transmission between the AV and interacting “Thing”. The framework relies on the neuro-batch learning algorithm to improve the consistency measure’s adaptability with the interacting “Things”. In the information-sharing process, the maximum extraction and utilisation are computed to track the AV with precise environmental knowledge. The interactions are batched with the type of traffic information obtained, such as population, accidents, objects, hindrances, etc. Throughout travel, the vehicle’s learning rate and the surrounding environment’s familiarity with it are classified. The learning neurons are connected to the information actuated and sensed by the AV to identify any unsafe vehicle activity in unknown or unidentified scenarios. Based on the risk and driving parameters, the safe and unsafe activity of the vehicles is categorised with a precise learning rate. Therefore, minor changes in vehicular decisions are monitored, and driving control is optimised accordingly to retain 7.93% of navigation assistance through a 9.76% high learning rate for different intervals. |
| format | Article |
| id | doaj-art-26f5dd357a0949ca817c346f368b17e1 |
| institution | Kabale University |
| issn | 2075-1702 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-26f5dd357a0949ca817c346f368b17e12024-11-26T18:11:07ZengMDPI AGMachines2075-17022024-11-01121179810.3390/machines12110798Ensuring Driving and Road Safety of Autonomous Vehicles Using a Control Optimiser Interaction Framework Through Smart “Thing” Information Sensing and ActuationAhmed Almutairi0Abdullah Faiz Al Asmari1Tariq Alqubaysi2Fayez Alanazi3Ammar Armghan4Department of Civil and Environmental Engineering, College of Engineering, Majmaah University, Majmaah 11952, Saudi ArabiaCivil Engineering Department, College of Engineering, King Khalid University, Abha 61421, Saudi ArabiaDepartment of Civil Engineering, College of Engineering, Northern Border University, Arar 73222, Saudi ArabiaCivil Engineering Department, College of Engineering, Jouf University, Sakakah 72388, Saudi ArabiaDepartment of Electrical Engineering, College of Engineering, Jouf University, Sakakah 72388, Saudi ArabiaRoad safety through point-to-point interaction autonomous vehicles (AVs) assimilate different communication technologies for reliable and persistent information sharing. Vehicle interaction resilience and consistency require novel sharing knowledge for retaining driving and pedestrian safety. This article proposes a control optimiser interaction framework (COIF) for organising information transmission between the AV and interacting “Thing”. The framework relies on the neuro-batch learning algorithm to improve the consistency measure’s adaptability with the interacting “Things”. In the information-sharing process, the maximum extraction and utilisation are computed to track the AV with precise environmental knowledge. The interactions are batched with the type of traffic information obtained, such as population, accidents, objects, hindrances, etc. Throughout travel, the vehicle’s learning rate and the surrounding environment’s familiarity with it are classified. The learning neurons are connected to the information actuated and sensed by the AV to identify any unsafe vehicle activity in unknown or unidentified scenarios. Based on the risk and driving parameters, the safe and unsafe activity of the vehicles is categorised with a precise learning rate. Therefore, minor changes in vehicular decisions are monitored, and driving control is optimised accordingly to retain 7.93% of navigation assistance through a 9.76% high learning rate for different intervals.https://www.mdpi.com/2075-1702/12/11/798autonomous vehicleintelligent transport systembatch optimisationdriving controlneural learningvehicle learning |
| spellingShingle | Ahmed Almutairi Abdullah Faiz Al Asmari Tariq Alqubaysi Fayez Alanazi Ammar Armghan Ensuring Driving and Road Safety of Autonomous Vehicles Using a Control Optimiser Interaction Framework Through Smart “Thing” Information Sensing and Actuation Machines autonomous vehicle intelligent transport system batch optimisation driving control neural learning vehicle learning |
| title | Ensuring Driving and Road Safety of Autonomous Vehicles Using a Control Optimiser Interaction Framework Through Smart “Thing” Information Sensing and Actuation |
| title_full | Ensuring Driving and Road Safety of Autonomous Vehicles Using a Control Optimiser Interaction Framework Through Smart “Thing” Information Sensing and Actuation |
| title_fullStr | Ensuring Driving and Road Safety of Autonomous Vehicles Using a Control Optimiser Interaction Framework Through Smart “Thing” Information Sensing and Actuation |
| title_full_unstemmed | Ensuring Driving and Road Safety of Autonomous Vehicles Using a Control Optimiser Interaction Framework Through Smart “Thing” Information Sensing and Actuation |
| title_short | Ensuring Driving and Road Safety of Autonomous Vehicles Using a Control Optimiser Interaction Framework Through Smart “Thing” Information Sensing and Actuation |
| title_sort | ensuring driving and road safety of autonomous vehicles using a control optimiser interaction framework through smart thing information sensing and actuation |
| topic | autonomous vehicle intelligent transport system batch optimisation driving control neural learning vehicle learning |
| url | https://www.mdpi.com/2075-1702/12/11/798 |
| work_keys_str_mv | AT ahmedalmutairi ensuringdrivingandroadsafetyofautonomousvehiclesusingacontroloptimiserinteractionframeworkthroughsmartthinginformationsensingandactuation AT abdullahfaizalasmari ensuringdrivingandroadsafetyofautonomousvehiclesusingacontroloptimiserinteractionframeworkthroughsmartthinginformationsensingandactuation AT tariqalqubaysi ensuringdrivingandroadsafetyofautonomousvehiclesusingacontroloptimiserinteractionframeworkthroughsmartthinginformationsensingandactuation AT fayezalanazi ensuringdrivingandroadsafetyofautonomousvehiclesusingacontroloptimiserinteractionframeworkthroughsmartthinginformationsensingandactuation AT ammararmghan ensuringdrivingandroadsafetyofautonomousvehiclesusingacontroloptimiserinteractionframeworkthroughsmartthinginformationsensingandactuation |