Creating Predictive Models for Forecasting the Accident Rate in Mountain Roads Using VANETs

Bordel, Borja; Rizzo, Gianluca; Jara, Antonio J.

doi:10.1007/978-3-319-73450-7_31

Cited by 4 publications

(4 citation statements)

References 18 publications

(17 reference statements)

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“…The authors [15] proposed a framework to estimate in advance about the number of possible road accidents which could happen according to the road condition by using the random variable function and Taylor's series. The authors [16] described another approach by breaking down the proposed model in four steps.…”

Section: A Driver Drowsiness and Monitoring Systemmentioning

confidence: 99%

Driver Drowsiness Detection and Monitoring System (DDDMS)

Rozali,

Fadilah,

Shariff

et al. 2022

IJACSA

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The purpose of this paper is to develop a driver drowsiness and monitoring system that could act as an assistant to the driver during the driving process. The system is aimed at reducing fatal crashes caused by driver's drowsiness and distraction. For drowsiness, the system operates by analysing eye blinks and yawn frequency of the driver while for distraction, the system works based on the head pose estimation and eye tracking. The alarm will be triggered if any of these conditions occur. Main part of the implementation of this system will be using python with computer vision, while Raspberry Pi, which is uniquely designed, for the hardware platform and the speaker for alarming. In short, this driver drowsiness monitoring system can always monitor drivers so as to avoid accidents in real time.

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Section: A Driver Drowsiness and Monitoring Systemmentioning

confidence: 99%

Driver Drowsiness Detection and Monitoring System (DDDMS)

Rozali,

Fadilah,

Shariff

et al. 2022

IJACSA

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“…Typical solutions based on historical data are heavy but highly precise mechanisms, supported by statistical estimators and specialized on global analyses and long-term behaviors [4]. Algorithms obtaining mean or most probable (common) parameters [1], dependencies among variables [12], or sets of all possible values [2] from databases with historical data are the most usual proposals. Massive data, on the other hand, are usually processed using hybrid Big Data and AI solutions [5].…”

Section: State Of the Artmentioning

confidence: 99%

A Predictor-Corrector Algorithm Based on Laurent Series for Biological Signals in the Internet of Medical Things

Bordel

You

2020

IEEE Access

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In future engineered systems for medical applications, a tight real-time integration between physical and computational processes will be required. That integration is achieved using feedback control loops which need high quality input data streams. However, hardware platforms can barely provide such high-quality data sequences (especially if mobile nodes are considered), and mechanisms to improve and polish physical and biological signals are then necessary. This paper proposes a predictor-corrector algorithm to improve the quality and precision of data (biological) signals in Internet of Medical Things deployments, especially if composed of mobile nodes. The proposed algorithm employs an Artificial Intelligence approach and statistical learning techniques to predict future data samples and correct errors in received information. Employed mathematical models follow a prediction-correction scheme and are based on complex functions, Laurent series and the idea of complex envelope. Simulation techniques are used to evaluate the performance of the proposed solution, showing that it improves the precision of traditional linear interpolation techniques up to 85%, and cubic splines up to 20%. Processing delay during operation is, for the referred precision, around 200ms.

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“…In [23], Bordel et al defined a model that predicted the accidents in mountain roads by analyzing the sensed weather data obtained from the weather forecasting office. The proposed model was based on Taylor's series and multivariate functions.…”

Section: Weather Based Approachesmentioning

confidence: 99%

Accident Prediction System Based on Hidden Markov Model for Vehicular Ad-Hoc Network in Urban Environments

et al. 2018

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With the emergence of autonomous vehicles and internet of vehicles (IoV), future roads of smart cities will have a combination of autonomous and automated vehicles with regular vehicles that require human operators. To ensure the safety of the road commuters in such a network, it is imperative to enhance the performance of Advanced Driver Assistance Systems (ADAS). Real-time driving risk prediction is a fundamental part of an ADAS. Many driving risk prediction systems have been proposed. However, most of them are based only on vehicle’s velocity. But in most of the accident scenarios, other factors are also involved, such as weather conditions or driver fatigue. In this paper, we proposed an accident prediction system for Vehicular ad hoc networks (VANETs) in urban environments, in which we considered the crash risk as a latent variable that can be observed using multi-observation such as velocity, weather condition, risk location, nearby vehicles density and driver fatigue. A Hidden Markov Model (HMM) was used to model the correlation between these observations and the latent variable. Simulation results showed that the proposed system has a better performance in terms of sensitivity and precision compared to state of the art single factor schemes.

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Creating Predictive Models for Forecasting the Accident Rate in Mountain Roads Using VANETs

Cited by 4 publications

References 18 publications

Driver Drowsiness Detection and Monitoring System (DDDMS)

Driver Drowsiness Detection and Monitoring System (DDDMS)

A Predictor-Corrector Algorithm Based on Laurent Series for Biological Signals in the Internet of Medical Things

Accident Prediction System Based on Hidden Markov Model for Vehicular Ad-Hoc Network in Urban Environments

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