A vehicle control system using a time synchronized Hybrid VANET to reduce road accidents caused by human error

Sam, Dahlia; Velanganni, Cyrilraj; Evangelin, T. Esther

doi:10.1016/j.vehcom.2016.11.001

Cited by 41 publications

(17 citation statements)

References 4 publications

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“…Thus, as complex task, driving demands both psychological and physiological undertaking where MWL is an ineluctable aspect [ 17 ]. A study dedicated to finding the causes of road accidents demonstrates that human error directly or indirectly contributes to 90% of the accidents [ 18 ]. Because of the association of driver’s MWL to committing errors while driving, and since these errors have been demonstrated as a principle contributing factor to road accidents, research on determining the in-drive MWL of drivers looks extremely urgent and important.…”

Section: Background and Related Workmentioning

confidence: 99%

A Novel Mutual Information Based Feature Set for Drivers’ Mental Workload Evaluation Using Machine Learning

et al. 2020

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Analysis of physiological signals, electroencephalography more specifically, is considered a very promising technique to obtain objective measures for mental workload evaluation, however, it requires a complex apparatus to record, and thus, with poor usability in monitoring in-vehicle drivers’ mental workload. This study proposes a methodology of constructing a novel mutual information-based feature set from the fusion of electroencephalography and vehicular signals acquired through a real driving experiment and deployed in evaluating drivers’ mental workload. Mutual information of electroencephalography and vehicular signals were used as the prime factor for the fusion of features. In order to assess the reliability of the developed feature set mental workload score prediction, classification and event classification tasks were performed using different machine learning models. Moreover, features extracted from electroencephalography were used to compare the performance. In the prediction of mental workload score, expert-defined scores were used as the target values. For classification tasks, true labels were set from contextual information of the experiment. An extensive evaluation of every prediction tasks was carried out using different validation methods. In predicting the mental workload score from the proposed feature set lowest mean absolute error was 0.09 and for classifying mental workload highest accuracy was 94%. According to the outcome of the study, it can be stated that the novel mutual information based features developed through the proposed approach can be employed to classify and monitor in-vehicle drivers’ mental workload.

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Section: Background and Related Workmentioning

confidence: 99%

A Novel Mutual Information Based Feature Set for Drivers’ Mental Workload Evaluation Using Machine Learning

et al. 2020

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“…Sam et al [12] proposed a VANET-based vehicle control system. The system was implemented by wirelessly connecting the vehicle nodes, pedestrian body units and the roadside units.…”

Section: Velocity Based Approachesmentioning

confidence: 99%

“…Velocity-Based: Collision avoidance system based only on vehicles' velocity, such as the one proposed by Sam et al [12], where the vehicles (also can be applied with pedestrians) contently broadcast their position to nearby vehicles. Any potential crash is detected based on the estimated speed (by computing GPS coordinates) and vehicles direction.…”

Section: Baselinesmentioning

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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“…Therefore, a novel vehicle control system combining Hybrid VANET which use a machine intervention as an effective alternative to replace the reaction of drivers was proposed by Sam, D. et al [1]. This system was developed integrating the hybrid VANET and the pedestrian body unit.…”

Section: Packets Lost Ratementioning

confidence: 99%

“…The increase of vehicles in the last decade has result in a large number of crashes and put lots of pressures on the existing road [1]. Pedestrians who are one of the most vulnerable participants between pedestrians and vehicles collisions, leading to fatal injuries and even fatalities [2].…”

Section: Introductionmentioning

confidence: 99%

Car-to-Pedestrian Communication Safety System Based on the Vehicular Ad-Hoc Network Environment: A Systematic Review

2017

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Abstract:With the unparalleled growth of motor vehicles, traffic accident between pedestrians and vehicles is one of the most serious issues in the word-wild. Plenty of injuries and fatalities are caused by the traffic accidents and crashes. The connected vehicular ad hoc network as an emerging approach which has the potential to reduce and even avoid accidents have been focused on by many researchers. A large number of car-to-pedestrian communication safety systems based on the vehicular ad hoc network are researching and developing. However, to our limited knowledge, a systematic review about the car-to-pedestrian communication safety system based on the vehicular ad-hoc network has not be written. The purpose and goal of this review is to systematically evaluate and access the reliability of car-to-pedestrian communication safety system based on the vehicular ad-hoc network environment and provide some recommendations for the future works according to throwing some light on the previous literatures. A quality evaluation was developed through established items and instruments tailored to this review. Future works are needed to focus on developing a valid as well as effective communication safety system based on the vehicular ad hoc network to protect the vulnerable road users.

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A vehicle control system using a time synchronized Hybrid VANET to reduce road accidents caused by human error

Cited by 41 publications

References 4 publications

A Novel Mutual Information Based Feature Set for Drivers’ Mental Workload Evaluation Using Machine Learning

A Novel Mutual Information Based Feature Set for Drivers’ Mental Workload Evaluation Using Machine Learning

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

Car-to-Pedestrian Communication Safety System Based on the Vehicular Ad-Hoc Network Environment: A Systematic Review

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