Location Prediction Model Based on the Internet of Vehicles for Assistance to Medical Vehicles

Abstract

Along with the Internet of Vehicles, some intelligent systems can help the medical vehicles transport medical supplies and patients. In terms of emergency issues like catastrophic natural disasters or serious accidents, safe and timely transportation for medical vehicles is particularly important. For assistance to medical vehicles on the road, models with position prediction can provide accurate position information of ambient vehicles in the next seconds. However, with the increasing number of vehicles on the road and the changing road environment, it is an important challenge to predict the location of vehicles in the road correctly. Current location prediction models for vehicles usually use the previous trajectory of vehicles, lacking the consideration of vehicle's state and real-time traffic information, which leads to relatively low accuracy and safety. Based on the deep belief nets (DBN) and long short-term memory (LSTM), this paper presents a location prediction model for assistance to medical vehicles (LPMVs), which fully considers vehicle's attributes, road information and driving environment as well as the relationship between the factors that influence vehicle driving behaviors and vehicle positions. By experiment, we prove that LPMVs can predict more accurately than current location prediction models and be a good choice for assistance to medical vehicles. INDEX TERMS Internet of Vehicles, medical vehicles, neural network, prediction model.

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

“…With this information, the simulation is performed to estimate false rate, accuracy, data utilisation, displacement error, and processing latency. Finally, these metrics are compared with the existing GMEPP [21], IVRN-DRL [26], and LPMV [33] methods discussed in the related works section.…”

“…Cheng et al [33] developed an IoT-based location prediction model for assistance to medical vehicles (LPMVs). The proposed model uses a combination of long short-term memory and deep belief nets to enhance the accuracy of medical vehicle processes.…”

“…Based on the literature analysis, there are several problems with existing methods in achieving less false rate, displacement error, processing latency and high accuracy, and data utilisation for varying displacement intervals such as GMEPP [21], IVRN-DRL [26], and LPMV [33] methods. Hence, this paper uses the NDP-AD method for analysing the opposite vehicle distance and traffic to provide accurate guidance independently.…”

The Efficient Mobile Management Based on Metaheuristic Algorithm for Internet of Vehicle

“…In addition, they also consider the relative order of location in the context to enhance the prediction accuracy. In [ 30 ], a location prediction model for assistance to medical vehicles was proposed, considering the best method for location prediction where the trajectory information is the only consideration. They take into account real-time traffic information and use deep belief networks and long short-term memory to achieve the goal of location prediction.…”

A Network-Centric Analysis for the Internet of Vehicles and Simulation Tools

“…Cheng et al [20] developed an urban-road connectivity model using possibility, data forwarding time, link forwarding capability, and packet error rate. Cheng et al [21] proposed a position prediction framework for IoV to be used by medical units. Their framework considered vehicle's attributes, road conditions, and driving load.…”