A Taxonomy for Car Accidents Predication Model Using Neural Networks

Ghazi Al-Naymat; Qurat ul Ain Nizamani; Shaymaa Ismail Ali; Anchal Shrestha; Hanspreet Kaur

doi:10.1007/978-3-031-35308-6_5

A Taxonomy for Car Accidents Predication Model Using Neural Networks

Ghazi Al-Naymat
, Qurat ul Ain Nizamani
, Shaymaa Ismail Ali
, Anchal Shrestha
, Hanspreet Kaur

College of Engineering and Information Technology

Kent Institute Australia
Cihan University-Erbil
Australian Catholic University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Traffic accident is a serious problem worldwide, causing human losses every year. Significant contributors to road accidents are road conditions, climate, unusual driving behaviors, drowsiness, and distraction while driving. In order to mitigate this problem, drivers can be facilitated with a prediction model that can assist them in avoiding accidents. There have been many developments in vehicle crash prediction, but they can be improved in terms of performance and accuracy. This paper suggests an accident prediction model based on Long short-term Neural Networks (LSTM) and Deep Convolution Neural Network (DCNN) Models. The proposed taxonomy allows the creation of a prediction model based on the components such as data, view, and prediction technique. Raw data captured from the gyroscope, speedometer, and smartphone camera is processed for speed estimation. Road facility detection is done through a smartphone-based intelligent Driving Device Recorder (DDR) system consisting of LSTM and CNN. DCNN model is used to analyse different kinds of road components such as traffic lights, crosswalks, stop lines, and pedestrians. Hence, this research critically analyses the works available on vehicle crash prediction using deep learning systems. Furthermore, an enhanced solution that can accurately predict the possible vehicle crash by analyzing the crash dataset using a deep neural network is proposed.

Original language	English
Title of host publication	Proceedings of the Second International Conference on Innovations in Computing Research (ICR’23)
Editors	Kevin Daimi, Abeer Al Sadoon
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	52-63
Number of pages	12
ISBN (Print)	9783031353079
DOIs	https://doi.org/10.1007/978-3-031-35308-6_5
State	Published - 2023
Event	2nd International Conference on Innovations in Computing Research, ICR 2023 - Madrid, Spain Duration: 4 Sep 2023 → 6 Sep 2023

Publication series

Name	Lecture Notes in Networks and Systems
Volume	721 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	2nd International Conference on Innovations in Computing Research, ICR 2023
Country/Territory	Spain
City	Madrid
Period	4/09/23 → 6/09/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being
SDG 11 Sustainable Cities and Communities

Keywords

Compress Convolution Neural Network
Deep Convolution Neural Network (CNN)
Deep Learning
Driving data recorder
LSTM
Scene understanding
Smartphone
Speedometer

Access to Document

10.1007/978-3-031-35308-6_5

Cite this

Al-Naymat, G., Nizamani, Q. U. A., Ali, S. I., Shrestha, A., & Kaur, H. (2023). A Taxonomy for Car Accidents Predication Model Using Neural Networks. In K. Daimi, & A. Al Sadoon (Eds.), Proceedings of the Second International Conference on Innovations in Computing Research (ICR’23) (pp. 52-63). (Lecture Notes in Networks and Systems; Vol. 721 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-35308-6_5

Al-Naymat, Ghazi ; Nizamani, Qurat ul Ain ; Ali, Shaymaa Ismail et al. / A Taxonomy for Car Accidents Predication Model Using Neural Networks. Proceedings of the Second International Conference on Innovations in Computing Research (ICR’23). editor / Kevin Daimi ; Abeer Al Sadoon. Springer Science and Business Media Deutschland GmbH, 2023. pp. 52-63 (Lecture Notes in Networks and Systems).

@inproceedings{5f454139716f43ffb04e1067c3ed48ef,

title = "A Taxonomy for Car Accidents Predication Model Using Neural Networks",

abstract = "Traffic accident is a serious problem worldwide, causing human losses every year. Significant contributors to road accidents are road conditions, climate, unusual driving behaviors, drowsiness, and distraction while driving. In order to mitigate this problem, drivers can be facilitated with a prediction model that can assist them in avoiding accidents. There have been many developments in vehicle crash prediction, but they can be improved in terms of performance and accuracy. This paper suggests an accident prediction model based on Long short-term Neural Networks (LSTM) and Deep Convolution Neural Network (DCNN) Models. The proposed taxonomy allows the creation of a prediction model based on the components such as data, view, and prediction technique. Raw data captured from the gyroscope, speedometer, and smartphone camera is processed for speed estimation. Road facility detection is done through a smartphone-based intelligent Driving Device Recorder (DDR) system consisting of LSTM and CNN. DCNN model is used to analyse different kinds of road components such as traffic lights, crosswalks, stop lines, and pedestrians. Hence, this research critically analyses the works available on vehicle crash prediction using deep learning systems. Furthermore, an enhanced solution that can accurately predict the possible vehicle crash by analyzing the crash dataset using a deep neural network is proposed.",

keywords = "Compress Convolution Neural Network, Deep Convolution Neural Network (CNN), Deep Learning, Driving data recorder, LSTM, Scene understanding, Smartphone, Speedometer",

author = "Ghazi Al-Naymat and Nizamani, \{Qurat ul Ain\} and Ali, \{Shaymaa Ismail\} and Anchal Shrestha and Hanspreet Kaur",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 2nd International Conference on Innovations in Computing Research, ICR 2023 ; Conference date: 04-09-2023 Through 06-09-2023",

year = "2023",

doi = "10.1007/978-3-031-35308-6\_5",

language = "English",

isbn = "9783031353079",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "52--63",

editor = "Kevin Daimi and \{Al Sadoon\}, Abeer",

booktitle = "Proceedings of the Second International Conference on Innovations in Computing Research (ICR{\textquoteright}23)",

address = "Germany",

}

Al-Naymat, G, Nizamani, QUA, Ali, SI, Shrestha, A & Kaur, H 2023, A Taxonomy for Car Accidents Predication Model Using Neural Networks. in K Daimi & A Al Sadoon (eds), Proceedings of the Second International Conference on Innovations in Computing Research (ICR’23). Lecture Notes in Networks and Systems, vol. 721 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 52-63, 2nd International Conference on Innovations in Computing Research, ICR 2023, Madrid, Spain, 4/09/23. https://doi.org/10.1007/978-3-031-35308-6_5

A Taxonomy for Car Accidents Predication Model Using Neural Networks. / Al-Naymat, Ghazi; Nizamani, Qurat ul Ain; Ali, Shaymaa Ismail et al.
Proceedings of the Second International Conference on Innovations in Computing Research (ICR’23). ed. / Kevin Daimi; Abeer Al Sadoon. Springer Science and Business Media Deutschland GmbH, 2023. p. 52-63 (Lecture Notes in Networks and Systems; Vol. 721 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A Taxonomy for Car Accidents Predication Model Using Neural Networks

AU - Al-Naymat, Ghazi

AU - Nizamani, Qurat ul Ain

AU - Ali, Shaymaa Ismail

AU - Shrestha, Anchal

AU - Kaur, Hanspreet

PY - 2023

Y1 - 2023

N2 - Traffic accident is a serious problem worldwide, causing human losses every year. Significant contributors to road accidents are road conditions, climate, unusual driving behaviors, drowsiness, and distraction while driving. In order to mitigate this problem, drivers can be facilitated with a prediction model that can assist them in avoiding accidents. There have been many developments in vehicle crash prediction, but they can be improved in terms of performance and accuracy. This paper suggests an accident prediction model based on Long short-term Neural Networks (LSTM) and Deep Convolution Neural Network (DCNN) Models. The proposed taxonomy allows the creation of a prediction model based on the components such as data, view, and prediction technique. Raw data captured from the gyroscope, speedometer, and smartphone camera is processed for speed estimation. Road facility detection is done through a smartphone-based intelligent Driving Device Recorder (DDR) system consisting of LSTM and CNN. DCNN model is used to analyse different kinds of road components such as traffic lights, crosswalks, stop lines, and pedestrians. Hence, this research critically analyses the works available on vehicle crash prediction using deep learning systems. Furthermore, an enhanced solution that can accurately predict the possible vehicle crash by analyzing the crash dataset using a deep neural network is proposed.

AB - Traffic accident is a serious problem worldwide, causing human losses every year. Significant contributors to road accidents are road conditions, climate, unusual driving behaviors, drowsiness, and distraction while driving. In order to mitigate this problem, drivers can be facilitated with a prediction model that can assist them in avoiding accidents. There have been many developments in vehicle crash prediction, but they can be improved in terms of performance and accuracy. This paper suggests an accident prediction model based on Long short-term Neural Networks (LSTM) and Deep Convolution Neural Network (DCNN) Models. The proposed taxonomy allows the creation of a prediction model based on the components such as data, view, and prediction technique. Raw data captured from the gyroscope, speedometer, and smartphone camera is processed for speed estimation. Road facility detection is done through a smartphone-based intelligent Driving Device Recorder (DDR) system consisting of LSTM and CNN. DCNN model is used to analyse different kinds of road components such as traffic lights, crosswalks, stop lines, and pedestrians. Hence, this research critically analyses the works available on vehicle crash prediction using deep learning systems. Furthermore, an enhanced solution that can accurately predict the possible vehicle crash by analyzing the crash dataset using a deep neural network is proposed.

KW - Compress Convolution Neural Network

KW - Deep Convolution Neural Network (CNN)

KW - Deep Learning

KW - Driving data recorder

KW - LSTM

KW - Scene understanding

KW - Smartphone

KW - Speedometer

UR - https://www.scopus.com/pages/publications/85164748458

U2 - 10.1007/978-3-031-35308-6_5

DO - 10.1007/978-3-031-35308-6_5

M3 - Conference contribution

AN - SCOPUS:85164748458

SN - 9783031353079

T3 - Lecture Notes in Networks and Systems

SP - 52

EP - 63

BT - Proceedings of the Second International Conference on Innovations in Computing Research (ICR’23)

A2 - Daimi, Kevin

A2 - Al Sadoon, Abeer

PB - Springer Science and Business Media Deutschland GmbH

T2 - 2nd International Conference on Innovations in Computing Research, ICR 2023

Y2 - 4 September 2023 through 6 September 2023

ER -

Al-Naymat G, Nizamani QUA, Ali SI, Shrestha A, Kaur H. A Taxonomy for Car Accidents Predication Model Using Neural Networks. In Daimi K, Al Sadoon A, editors, Proceedings of the Second International Conference on Innovations in Computing Research (ICR’23). Springer Science and Business Media Deutschland GmbH. 2023. p. 52-63. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-35308-6_5

A Taxonomy for Car Accidents Predication Model Using Neural Networks

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this