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Artificial Neural Network Analysis in Road Crash Data: A Review on Its Potential Application in Autonomous Vehicles

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Intelligent Manufacturing and Mechatronics (iM3F 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 850))

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Abstract

This review examines the utilization of Artificial Neural Networks (ANNs) in the analysis of road crash data and their prospective contribution to autonomous vehicles. Artificial neural networks (ANNs) have exhibited considerable promise in the realm of modeling and forecasting crash occurrences, thereby offering valuable insights for enhancing road safety. Artificial neural networks (ANNs) play a crucial role in the functionality of autonomous vehicles by enabling them to effectively perceive their surroundings, make informed decisions, and operate with a high level of safety and efficiency. Nevertheless, the deployment of Artificial Neural Networks (ANNs) encounters various obstacles, such as their inherent opaqueness, the necessity for substantial quantities of meticulously curated data, and the demanding computational capabilities they demand. Possible solutions to these challenges encompass the advancement of methodologies for interpreting artificial neural networks (ANNs) and the utilization of more intricate ANN models. Notwithstanding the advancements achieved in this particular domain, it is imperative to acknowledge and rectify the existing deficiencies in the present body of research. These encompass the necessity for conducting more extensive research on the utilization of Artificial Neural Networks (ANNs) in the analysis of road crashes, the requirement for developing more resilient testing methodologies for these systems, and the demand for further investigation into the effective implementation of ANNs in autonomous vehicles. This review makes a significant contribution to the current academic conversation in the field, offering valuable insights for researchers, policymakers, and practitioners engaged in the domains of road safety and autonomous vehicles.

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Acknowledgements

The author wishes to express profound gratitude to the Ministry of Higher Education for their generous support of this study through the Fundamental Research Grant Scheme (FRGS), under the grant number FRGS/1/2022/TK0/UMP/02/67. This research would not have been possible without their commitment to fostering academic exploration and innovation. Additionally, heartfelt thanks are extended to the Universiti Malaysia Pahang Al-Sultan Abdullah for their invaluable support and financial assistance under the grant RDU Number RDU220317.

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Authors and Affiliations

  1. Faculty of Electrical and Electronic Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600, Pekan, Pahang, Malaysia

    Syukran Hakim Norazman, Mohd Amir Shahlan Mohd Aspar & Abdul Nasir Abd. Ghafar

  2. Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, 26600, Pekan, Pahang, Malaysia

    Norasilah Karumdin & Ahmad Noor Syukri Zainal Abidin

Authors
  1. Syukran Hakim Norazman
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  2. Mohd Amir Shahlan Mohd Aspar
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  3. Abdul Nasir Abd. Ghafar
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  4. Norasilah Karumdin
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  5. Ahmad Noor Syukri Zainal Abidin
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Corresponding author

Correspondence to Abdul Nasir Abd. Ghafar .

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Editors and Affiliations

  1. Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan, Malaysia

    Wan Hasbullah Mohd. Isa

  2. Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan, Malaysia

    Ismail Mohd. Khairuddin

  3. Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan, Malaysia

    Mohd. Azraai Mohd. Razman

  4. Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, Pekan, Malaysia

    Sarah 'Atifah Saruchi

  5. School of Intelligent Manufacturing Ecosystem, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Sze-Hong Teh

  6. Department of Computer Science, University of York, York, UK

    Pengcheng Liu

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Norazman, S.H., Aspar, M.A.S.M., Abd. Ghafar, A.N., Karumdin, N., Abidin, A.N.S.Z. (2024). Artificial Neural Network Analysis in Road Crash Data: A Review on Its Potential Application in Autonomous Vehicles. In: Mohd. Isa, W.H., Khairuddin, I.M., Mohd. Razman, M.A., Saruchi, S.'., Teh, SH., Liu, P. (eds) Intelligent Manufacturing and Mechatronics. iM3F 2023. Lecture Notes in Networks and Systems, vol 850. Springer, Singapore. https://doi.org/10.1007/978-981-99-8819-8_8

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  • DOI: https://doi.org/10.1007/978-981-99-8819-8_8

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