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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Machines (Basel) 2024-11, Vol.12 (11), p.798
Hauptverfasser: Almutairi, Ahmed, Asmari, Abdullah Faiz Al, Alqubaysi, Tariq, Alanazi, Fayez, Armghan, Ammar
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung: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.
ISSN:2075-1702
2075-1702
DOI:10.3390/machines12110798