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AI Enabled Accident Detection and Alert System Using IoT and Deep Learning for Smart Cities

Author

Listed:
  • Nikhlesh Pathik

    (Computer Science and Engineering, Sagar Institute of Science & Technology, Bhopal 462030, India)

  • Rajeev Kumar Gupta

    (Computer Science and Engineering, Pandit Deendayal Petroleum University, Gandhinagar 382007, India)

  • Yatendra Sahu

    (Computer Science and Engineering Indian Institute of Information Technology, Bhopal 462003, India)

  • Ashutosh Sharma

    (Institute of Computer Technology and Information Security, Southern Federal University, 344006 Rostov Oblast, Russia
    School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248171, India)

  • Mehedi Masud

    (Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

  • Mohammed Baz

    (Department of Computer Engineering, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia)

Abstract

As the number of vehicles increases, road accidents are on the rise every day. According to the World Health Organization (WHO) survey, 1.4 million people have died, and 50 million people have been injured worldwide every year. The key cause of death is the unavailability of medical care at the accident site or the high response time in the rescue operation. A cognitive agent-based collision detection smart accident alert and rescue system will help us to minimize delays in a rescue operation that could save many lives. With the growing popularity of smart cities, intelligent transportation systems (ITS) are drawing major interest in academia and business, and are considered as a means to improve road safety in smart cities. This article proposed an intelligent accident detection and rescue system which mimics the cognitive functions of the human mind using the Internet of Things (IoTs) and the Artificial Intelligence system (AI). An IoT kit is developed that detects the accident and collects all accident-related information, such as position, pressure, gravitational force, speed, etc., and sends it to the cloud. In the cloud, once the accident is detected, a deep learning (DL) model is used to validate the output of the IoT module and activate the rescue module. Once the accident is detected by the DL module, all the closest emergency services such as the hospital, police station, mechanics, etc., are notified. Ensemble transfer learning with dynamic weights is used to minimize the false detection rate. Due to the dataset’s unavailability, a personalized dataset is generated from the various videos available on the Internet. The proposed method is validated by a comparative analysis of ResNet and InceptionResnetV2. The experiment results show that InceptionResnetV2 provides a better performance compared to ResNet with training, validation, and a test accuracy of 98%, respectively. To measure the performance of the proposed approach in the real world, it is validated on the toy car.

Suggested Citation

  • Nikhlesh Pathik & Rajeev Kumar Gupta & Yatendra Sahu & Ashutosh Sharma & Mehedi Masud & Mohammed Baz, 2022. "AI Enabled Accident Detection and Alert System Using IoT and Deep Learning for Smart Cities," Sustainability, MDPI, vol. 14(13), pages 1-24, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:13:p:7701-:d:846616
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    References listed on IDEAS

    as
    1. M. Mazhar Rathore & Anand Paul & Awais Ahmad & Gwanggil Jeon, 2017. "IoT-Based Big Data: From Smart City towards Next Generation Super City Planning," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 13(1), pages 28-47, January.
    2. Yun Xie & Binggeng Xie & Ziwei Wang & Rajeev Kumar Gupta & Mohammed Baz & Mohammed A. AlZain & Mehedi Masud, 2022. "Geological Resource Planning and Environmental Impact Assessments Based on GIS," Sustainability, MDPI, vol. 14(2), pages 1-12, January.
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    Cited by:

    1. Jonathan Gumz & Diego Castro Fettermann & Enzo Morosini Frazzon & Mirko Kück, 2022. "Using Industry 4.0’s Big Data and IoT to Perform Feature-Based and Past Data-Based Energy Consumption Predictions," Sustainability, MDPI, vol. 14(20), pages 1-34, October.

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