Explainable navigation system using fuzzy reinforcement learning

Bautista-Montesano, Rolando; Bustamante-Bello, Rogelio; Ramírez-Mendoza, Ricardo A.

doi:10.1007/s12008-020-00717-1

Cited by 9 publications

(1 citation statement)

References 48 publications

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“…The clustering technique is used in support vector machines to classify huge data sets and produce sequenced and detailed data for the analysis process. It also identifies the traffic flow of vehicles for an emergency vehicle navigation system which provides an actual set of data for the analysis and detection process [19,20].…”

Section: Introductionmentioning

confidence: 99%

Emergency Vehicle Driving Assistance System Using Recurrent Neural Network with Navigational Data Processing Method

Anjum

Shahab

2023

Sustainability

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Emergency vehicle transportation is important for responding to and transporting individuals during emergencies. This type of transportation faces several issues, such as road safety, navigation and communication, time-critical operations, resource utilisation, traffic congestion, data processing and analysis, and individual safety. Vehicle navigation and coordination is a critical aspect of emergency response that involves guiding emergency vehicles, such as ambulances, to the location of an emergency or medical centre as quickly and safely as possible. Therefore, it requires additional effort to reduce driving risks. The roadside units support emergency vehicles and infrastructure to decrease collisions and enhance optimal navigation routes. However, during the emergency vehicle’s data communication and navigation process, communication is interrupted due to vehicle outages. Therefore, this study proposes the Navigation Data Processing for Assisted Driving (NDP-AD) method to address the problem. The proposed approach assimilates infrastructure and neighbouring location information during driving. The integrated information is processed for distance and traffic during the previous displacement interval. The NDP-AD method employs a recurrent neural network learning approach to analyse opposing vehicle distance and traffic to provide accurate, independent guidance. This effective learning-based guidance process minimises false navigations and deviation in displacement. System efficiency is evaluated based on processing latency, displacement error, data utilisation, false rate, and accuracy metrics.

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Section: Introductionmentioning

confidence: 99%