Real-time vehicular accident prevention system using deep learning architecture

Kabir, M. Ashraful; Roy, Sahadev

doi:10.1016/j.eswa.2022.117837

Cited by 14 publications

(3 citation statements)

References 25 publications

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“…Concerning predicting the severity of accidents, neural networks have been used to create models capable of predicting the severity of road accidents [12], [13]. In accident prevention, Kabir et Roy [14] developed an algorithm based on deep learning for real-time collision avoidance. Fan et al [15] used SVM and deep neural networks to develop an algorithm for identifying and analyzing traffic accident black spots.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of the Accidents Risk Caused by Truck Drivers using a Fuzzy Bayesian Approach

Benallou,

Azmani,

Azmani

2023

IJACSA

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Road accidents cause hundreds of fatalities and injuries each year; due to their size and operating features, heavy trucks typically experience more severe accidents. Many factors are likely to cause such accidents; however, statistics mainly blame human error. This paper analyses the risk of accidents for heavy vehicles, focusing on driver-related factors contributing to accidents. A model is developed to anticipate the probability of an accident by using Bayesian networks (BNs) and fuzzy logic. Three axioms were verified to validate the developed model, and a sensitivity analysis is performed to identify the factors that have the most significant influence over truck accidents. Subsequently, the result provided by the model was exploited to examine the effects of in-vehicle road safety systems in preventing road accidents via an event tree analysis. The results underlined a strong link between the occurrence of accidents and parameters related to the driver, such as alcohol and substance consumption, his driving style, and his reactivity. Similarly, unfavourable working conditions significantly impact the occurrence of accidents since it contributes to fatigue, one of the leading causes of road accidents. Also, the event tree analysis results have highlighted the importance of equipping trucks with these mechanisms.

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Section: Related Workmentioning

confidence: 99%

Evaluation of the Accidents Risk Caused by Truck Drivers using a Fuzzy Bayesian Approach

Benallou,

Azmani,

Azmani

2023

IJACSA

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“…This method provides better range coverage and enables improved perception through 3D object classification and detection. In [16], the authors introduced real-time object identification, distance estimation, and instantaneous position tracking in all environmental conditions using a deep learning algorithm with no additional sensors. The proposed framework was implemented on a Raspberry Pi 4 Model B using the Raspberry Pi NoIR Camera Module V2.…”

Section: Related Workmentioning

confidence: 99%

Vehicular Environment Identification Based on Channel State Information and Deep Learning

Ribouh

Sadli

Elhillali

et al. 2022

Sensors

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This paper presents a novel vehicular environment identification approach based on deep learning. It consists of exploiting the vehicular wireless channel characteristics in the form of Channel State Information (CSI) in the receiver side of a connected vehicle in order to identify the environment type in which the vehicle is driving, without any need to implement specific sensors such as cameras or radars. We consider environment identification as a classification problem, and propose a new convolutional neural network (CNN) architecture to deal with it. The estimated CSI is used as the input feature to train the model. To perform the identification process, the model is targeted for implementation in an autonomous vehicle connected to a vehicular network (VN). The proposed model is extensively evaluated, showing that it can reliably recognize the surrounding environment with high accuracy (96.48%). Our model is compared to related approaches and state-of-the-art classification architectures. The experiments show that our proposed model yields favorable performance compared to all other considered methods.

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“…One of the promising solutions for controlling compliance with the speed limit on roads is the use of traffic camera systems. These systems, based on advanced technologies, provide effective traffic monitoring and detect violations such as speeding, which are certainly one of the most common causes of road accidents [2].…”

Section: Introductionmentioning

confidence: 99%

Automated System for Determining Speed of Cars Ahead

Pavlova,

Bilinska,

Holovatiuk

et al. 2023

CSIT

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number of accidents, researchers and engineers are constantly working on the introduction of new technologies. One of the significant innovations is the speed control system based on traffic cameras use. This paper gives a detailed overview of speed control systems role and importance on the roads. The principles of its operation, advantages, as well as methods of increasing the efficiency of this system in order to achieve optimal results in speed control and ensuring road safety are also analyzed in detail.

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Real-time vehicular accident prevention system using deep learning architecture

Cited by 14 publications

References 25 publications

Evaluation of the Accidents Risk Caused by Truck Drivers using a Fuzzy Bayesian Approach

Evaluation of the Accidents Risk Caused by Truck Drivers using a Fuzzy Bayesian Approach

Vehicular Environment Identification Based on Channel State Information and Deep Learning

Automated System for Determining Speed of Cars Ahead

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