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Riot Perception and Safety Navigation of Autonomous Vehicles Using Deep Learning

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Proceedings of the 2nd International Conference on Big Data, IoT and Machine Learning (BIM 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 867))

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Abstract

Rioting is an act of participating in a violent public disturbance, which involves multiple individuals engaging in destructive activities. Such activities can include vandalism, theft from both public and private property, physical assaults on others, and looting. Riots can significantly harm both government and public property, resulting in losses of life, injuries, and property damage. Most of the time, it has been observed that private and public transport turned into the major targets of riots. By detecting potential threats and responding quickly, autonomous vehicles equipped with riot prevention features can help to prevent harm to both individuals and property during a riot. Moreover, riot threat-detecting features can contribute to minimizing the economic impact of riots, which is particularly important for businesses and communities that rely on tourism, trade, and commerce. Despite the development of various safety features in autonomous vehicles, there is currently a lack of effective measures to detect riots and violent public disturbances on roads and highways. In this study, we propose a solution for leveraging the You Only Look Once (YOLO) algorithm to detect six types of road objects and one class of threats for autonomous vehicles. The YOLO version 8 model was trained and assessed on a dataset of road objects including riot threats, and it achieved a maximum accuracy of 97.71%. Additionally, the proposed solution can be coupled with ground robots and unmanned aerial vehicles technology to enable real-time monitoring and treatment of chaotic and risky zones of riot.

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References

  1. Komol MMR et al. Deep RNN based prediction of driver’s intended movements at intersection using cooperative awareness messages. IEEE Trans Intell Transp Syst. https://doi.org/10.1109/TITS.2023.3254905

  2. Komol MM, Sagar MS, Mohammad N, Pinnow J, Elhenawy M, Masoud M, Glaser S, Liu SQ (2021) Simulation study on an ICT-based maritime management and safety framework for movable bridges. Appl Sci 11(16):7198

    Article  Google Scholar 

  3. Khan MT, Komol MMR, Podder AK, Mishu SA (2019) A developed length based product separating conveyor for industrial automation. In: International conference on electrical, communication, electronics, instrumentation and computing (ICECEIC)

    Google Scholar 

  4. Komol MMR et al (2022) A review on drivers’ red light running behavior predictions and technology based countermeasures. IEEE Access 10:25309–25326. https://doi.org/10.1109/ACCESS.2022.3154088

    Article  Google Scholar 

  5. Rahman MM, Mamun SA (2022) Prior prediction and management of autism in child through behavioral analysis using machine learning approach. In: Rhythms in healthcare. Springer Nature Singapore, Singapore, pp 63–77

    Google Scholar 

  6. Reza SM, Rahman MM, Al Mamun S (2014) A new approach for road networks—a vehicle xml device collaboration with big data. In: 2014 international conference on electrical engineering and information & communication technology, Apr 2014. IEEE, pp 1–5

    Google Scholar 

  7. Meghla TI, Rahman MM, Biswas AA, Hossain JT, Khatun T (2021) Supply chain management with demand forecasting of covid-19 vaccine using blockchain and machine learning. In: 2021 12th international conference on computing communication and networking technologies (ICCCNT), July 2021. IEEE, pp 01–07

    Google Scholar 

  8. Hossain MR, Whaiduzzaman M, Barros A, Tuly SR, Mahi MJN, Roy S et al (2021) A scheduling-based dynamic fog computing framework for augmenting resource utilization. Simul Model Pract Theory 111:102336

    Article  Google Scholar 

  9. Whaiduzzaman M, Hossain MR, Shovon AR, Roy S, Laszka A, Buyya R, Barros A (2020) A privacy-preserving mobile and fog computing framework to trace and prevent COVID-19 community transmission. IEEE J Biomed Health Inform 24(12):3564–3575

    Article  Google Scholar 

  10. The future of mobility is at our doorstep. McKinsey & Company. [Online]. Available: https://www.mckinsey.com/~/media/McKinsey/Industries/Automotive%20and%20Assembly/Our%20Insights/The%20future%20of%20mobility%20is%20at%20our%20doorstep/The-future-of-mobility-is-at-our-doorstep.ashx. Accessed 29 Apr 2023

  11. Laukkonen J (2020) Take a ride in Waymo’s self-driving car. Lifewire, 13 Feb 2020. [Online]. Available: https://www.lifewire.com/waymo-self-driving-cars-4171314. Accessed 29 Apr 2023

  12. Komol MMR et al (2021) Crash severity analysis of vulnerable road users using machine learning. PLoS ONE

    Google Scholar 

  13. Silmi Juman ZA, Masoud M, Elhenawy M, Bhuiyan H, Komol MM, Battaïa O (2021) A new algorithm for solving uncapacitated transportation problem with interval-defined demands and suppliers capacities. J Intell Fuzzy Syst 41(1):625–637

    Google Scholar 

  14. Wikipedia (2020). [Online]. Available: https://en.wikipedia.org/wiki/Aftermath_of_the_George_Floyd_protests_in_Minneapolis%E2%80%93Saint_Paul#cite_note-Penrod_et_al_(13_July_2020)-5

  15. Bendali-Braham M, Weber J, Forestier G, Idoumghar L, Muller P-A (2021) Recent trends in crowd analysis: a review. Mach Learn Appl 4

    Google Scholar 

  16. Bhuiyan MR, Abdullah J, Hashim N, Farid FA (2022) Video analytics using deep learning for crowd analysis: a review. Springer 81:27895–27922

    Google Scholar 

  17. Sonkar R, Rathod S, Jadhav R, Patil D (2020) Crowd abnormal behaviour detection using deep learning. In: International conference on automation, computing and communication 2020 (ICACC-2020)

    Google Scholar 

  18. Dabhi M, Shah M, Bharti P, Puvar P, Prajapati B (2020) Unusual activity detection in crowd using deep learning. J Emerg Technol Innov Res (JETIR) 7(6):470–476

    Google Scholar 

  19. Sabbella SR (2020) Fire and smoke detection for smart cities using deep neural networks and edge computing on embedded sensors. Sapienza University of Rome, Rome

    Google Scholar 

  20. Lim J, Al Jobayer MI, Baskaran VM, Lim JM, Wong K, See J (2019) Gun detection in surveillance videos using deep neural networks. In: 2019 Asia-Pacific signal and information processing association annual summit and conference (APSIPA ASC), Lanzhou, China

    Google Scholar 

  21. Khan A, Shah JA, Kadir K, Albattah W, Khan F (2020) Crowd monitoring and localization using deep convolutional neural network: a review. Appl Sci

    Google Scholar 

  22. Lloyd K, Marshall D, Moore SC, Rosin PL (2017) Detecting violent and abnormal crowd activity using temporal analysis of grey level co-occurrence matrix (GLCM) based texture measures. arXiv

    Google Scholar 

  23. Olmos R, Tabik S, Herrera F (2017) Automatic handgun detection alarm in videos using deep learning. arXiv

    Google Scholar 

  24. Cai G, Wu H, Lv R (2014) Rumors detection in Chinese via crowd responses. In: IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM 2014), Beijing, China

    Google Scholar 

  25. Hasan MS, Fahim F, Farabi AA, Rahman F (2022) A comparison between YOLOv7 and YOLOv5-based detection of combat targets and camouflaged militia. In: 2022 4th international conference on sustainable technologies for industry 4.0 (STI), Dhaka, Bangladesh, pp 1–6. https://doi.org/10.1109/STI56238.2022.10103281

  26. Lamba SNN (2017) Crowd monitoring and classification: a survey. In: Advances in computer and computational sciences. Advances in intelligent systems and computing, Singapore

    Google Scholar 

  27. Marsden M, McGuinness K, Little S, O’Connor NE (2016) Holistic features for real-time crowd behaviour anomaly detection. In: 2016 IEEE international conference on image processing (ICIP), Phoenix, AZ, pp 918–922. https://doi.org/10.1109/ICIP.2016.7532491

  28. Mostafizur Rahman Komol M, Sabid Hasan M, Ali S (2023) Sugarcane diseases identification and detection via machine learning. In: Bansal JC, Uddin MS (eds) Computer vision and machine learning in agriculture, vol 3. Algorithms for intelligent systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-3754-7_37

  29. Tzelepi ATM (2021) Graph embedded convolutional neural networks in human crowd detection for drone flight safety. IEEE Trans Emerg Top Comput Intell 5(2):191–204

    Article  Google Scholar 

  30. Kumar A, Sureshkumar C (2014) Abnormal crowd detection and tracking in surveillance video sequences. Int J Adv Res Comput Commun Eng 3(9):7935–7939

    Google Scholar 

  31. Dataset link. https://www.kaggle.com/datasets/sabidhasanarrow/riot-detection-dataset

  32. Hasan MM, Hasan N, Alsubaie MS, Komol MM (2021) Diagnosis of tobacco addiction using medical signal: an EEG-based time-frequency domain analysis using machine learning. Adv Sci Technol Eng Syst J 6(1):842–849

    Article  Google Scholar 

  33. Fahim F, Al Farabi A, Hasan MS, Hasan MM (2022) Diagnosis of diabetes using clinical features: an analysis based on machine learning techniques. In: 2022 3rd international informatics and software engineering conference (IISEC), Ankara, Turkey, pp 1–5. https://doi.org/10.1109/IISEC56263.2022.9998257

  34. Komol MMR, Podder AK (2017) Design and construction of product separating conveyor based on color. In: 2017 3rd international conference on electrical information and communication technology (EICT), Khulna, Bangladesh, pp 1–5. https://doi.org/10.1109/EICT.2017.8275163

  35. Arafat ME, Larue GS, Dehkordi SG (2022) Effectiveness of interventions for mobile phone distracted pedestrians: a systematic review. J Saf Res

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Electrical Engineering and Robotics, Queensland University of Technology (QUT), Brisbane, Australia

    Md. Mostafizur Rahman Komol

  2. Data61 Robotics Group, The Commonwealth Scientific and Industrial Research Organization (CSIRO), Canberra, Australia

    Md. Mostafizur Rahman Komol

  3. Department of Electrical and Electronic Engineering, Bangladesh Army University of Engineering and Technology, Natore, Bangladesh

    Md. Sabid Hasan

  4. School of Information Systems, Queensland University of Technology (QUT), Brisbane, Australia

    Md. Razon Hossain & Md. Mahfujur Rahman

  5. School of Civil and Environmental Engineering, Queensland University of Technology (QUT), Brisbane, Australia

    Md. Eaysir Arafat

  6. Department of Computer Science and Engineering, Chittagong University of Engineering and Technology, Chittagong, Bangladesh

    Mohammad Shamsul Arefin

  7. Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh

    Md. Mahfujur Rahman

Authors
  1. Md. Mostafizur Rahman Komol
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  2. Md. Sabid Hasan
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  3. Md. Razon Hossain
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  4. Md. Eaysir Arafat
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  5. Mohammad Shamsul Arefin
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  6. Md. Mahfujur Rahman
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Corresponding author

Correspondence to Md. Mahfujur Rahman .

Editor information

Editors and Affiliations

  1. Chittagong University of Engineering and Technology, Chittagong, Bangladesh

    Mohammad Shamsul Arefin

  2. Jahangirnagar University, Dhaka, Bangladesh

    M. Shamim Kaiser

  3. Daffodil International University, Dhaka, Bangladesh

    Touhid Bhuiyan

  4. Department of Computer Science and Engineering, Techno International New Town, Kolkata, India

    Nilanjan Dey

  5. Nottingham Trent University, Nottingham, UK

    Mufti Mahmud

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Komol, M.M.R., Hasan, M.S., Hossain, M.R., Arafat, M.E., Arefin, M.S., Rahman, M.M. (2024). Riot Perception and Safety Navigation of Autonomous Vehicles Using Deep Learning. In: Arefin, M.S., Kaiser, M.S., Bhuiyan, T., Dey, N., Mahmud, M. (eds) Proceedings of the 2nd International Conference on Big Data, IoT and Machine Learning. BIM 2023. Lecture Notes in Networks and Systems, vol 867. Springer, Singapore. https://doi.org/10.1007/978-981-99-8937-9_5

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  • DOI: https://doi.org/10.1007/978-981-99-8937-9_5

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-99-8936-2

  • Online ISBN: 978-981-99-8937-9

  • eBook Packages: EngineeringEngineering (R0)

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