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Emergency Vehicle Driving Assistance System Using Recurrent Neural Network with Navigational Data Processing Method

Author

Listed:
  • Mohd Anjum

    (Department of Computer Engineering, Aligarh Muslim University, Aligarh 202002, India)

  • Sana Shahab

    (Department of Business Administration, College of Business Administration, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia)

Abstract

Emergency vehicle transportation is important for responding to and transporting individuals during emergencies. This type of transportation faces several issues, such as road safety, navigation and communication, time-critical operations, resource utilisation, traffic congestion, data processing and analysis, and individual safety. Vehicle navigation and coordination is a critical aspect of emergency response that involves guiding emergency vehicles, such as ambulances, to the location of an emergency or medical centre as quickly and safely as possible. Therefore, it requires additional effort to reduce driving risks. The roadside units support emergency vehicles and infrastructure to decrease collisions and enhance optimal navigation routes. However, during the emergency vehicle’s data communication and navigation process, communication is interrupted due to vehicle outages. Therefore, this study proposes the Navigation Data Processing for Assisted Driving (NDP-AD) method to address the problem. The proposed approach assimilates infrastructure and neighbouring location information during driving. The integrated information is processed for distance and traffic during the previous displacement interval. The NDP-AD method employs a recurrent neural network learning approach to analyse opposing vehicle distance and traffic to provide accurate, independent guidance. This effective learning-based guidance process minimises false navigations and deviation in displacement. System efficiency is evaluated based on processing latency, displacement error, data utilisation, false rate, and accuracy metrics.

Suggested Citation

  • Mohd Anjum & Sana Shahab, 2023. "Emergency Vehicle Driving Assistance System Using Recurrent Neural Network with Navigational Data Processing Method," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:4:p:3069-:d:1061537
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    References listed on IDEAS

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    1. Wu, Jiaming & Kulcsár, Balázs & Ahn, Soyoung & Qu, Xiaobo, 2020. "Emergency vehicle lane pre-clearing: From microscopic cooperation to routing decision making," Transportation Research Part B: Methodological, Elsevier, vol. 141(C), pages 223-239.
    2. Han-Tao Zhao & Xin Zhao & Liu-Yan Xin, 2020. "Cellular automaton model for three-lane urban road considering Internet of Vehicles lane," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 31(12), pages 1-24, December.
    3. Muhammad Muhitur Rahman & Md Kamrul Islam & Ammar Al-Shayeb & Md Arifuzzaman, 2022. "Towards Sustainable Road Safety in Saudi Arabia: Exploring Traffic Accident Causes Associated with Driving Behavior Using a Bayesian Belief Network," Sustainability, MDPI, vol. 14(10), pages 1-22, May.
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