Machine Learning for Autonomous Vehicle Real-time Traffic Monitoring and Analysis
Cover
PDF

How to Cite

[1]
Dr. Alexandre Bayen, “Machine Learning for Autonomous Vehicle Real-time Traffic Monitoring and Analysis”, Journal of Bioinformatics and Artificial Intelligence, vol. 1, no. 1, pp. 40–53, Jun. 2024, Accessed: Sep. 19, 2024. [Online]. Available: https://biotechjournal.org/index.php/jbai/article/view/20

Abstract

This paper intends to provide a state-of-the-art literature review that covers topics pertinent to autonomous vehicles and real-time traffic monitoring and analyse research relating to these areas. Several autonomous vehicle topics were recognised including, but not limited to the following: fine pattern concerning the simulation, validation research and control techniques for unexpected autonomous vehicle movement, disaster response considerations and their enhancements, a Pavelbravo obstacle avoiding algorithm, ant colony system improved technical strategies and implementation plan development for sub-micrometer precision. Moreover autonomous vehicle topics were found influencing real-world visions, delivery traffic organisation, an everyday modification of car sales and traffic accident forecast. This review finds an analysis of contemporary research across these pertinent areas can be used to identify knowledge challenges and potential measures to overcome such challenges.

PDF

References

Y. Zhu, M. Wang, X. Yin, J. Zhang et al., "Deep Learning in Diverse Intelligent Sensor Based Systems," 2022. ncbi.nlm.nih.gov

Mahammad Shaik, et al. “Envisioning Secure and Scalable Network Access Control: A Framework for Mitigating Device Heterogeneity and Network Complexity in Large-Scale Internet-of-Things (IoT) Deployments”. Distributed Learning and Broad Applications in Scientific Research, vol. 3, June 2017, pp. 1-24, https://dlabi.org/index.php/journal/article/view/1.

Tatineni, Sumanth. "Beyond Accuracy: Understanding Model Performance on SQuAD 2.0 Challenges." International Journal of Advanced Research in Engineering and Technology (IJARET) 10.1 (2019): 566-581.

Vemoori, V. “Towards Secure and Trustworthy Autonomous Vehicles: Leveraging Distributed Ledger Technology for Secure Communication and Exploring Explainable Artificial Intelligence for Robust Decision-Making and Comprehensive Testing”. Journal of Science & Technology, vol. 1, no. 1, Nov. 2020, pp. 130-7, https://thesciencebrigade.com/jst/article/view/224.

D. Impedovo, F. Balducci, V. Dentamaro, and G. Pirlo, "Vehicular Traffic Congestion Classification by Visual Features and Deep Learning Approaches: A Comparison," 2019. ncbi.nlm.nih.gov

E. Qasemi and A. Oltramari, "Intelligent Traffic Monitoring with Hybrid AI," 2022. [PDF]

M. Aqib, R. Mehmood, A. Alzahrani, I. Katib et al., "Smarter Traffic Prediction Using Big Data, In-Memory Computing, Deep Learning and GPUs," 2019. ncbi.nlm.nih.gov

S. Almeida Carneiro, G. Chierchia, A. Pirayre, and L. Najman, "Clustering Dynamics for Improved Speed Prediction Deriving from Topographical GPS Registrations," 2024. [PDF]

D. R. Patrikar and M. Rajaram Parate, "Anomaly detection using edge computing in video surveillance system: review," 2022. ncbi.nlm.nih.gov

G. Kim and S. Kim, "A Road Defect Detection System Using Smartphones," 2024. ncbi.nlm.nih.gov

S. Bakheet and A. Al-Hamadi, "A deep neural framework for real-time vehicular accident detection based on motion temporal templates," 2022. ncbi.nlm.nih.gov

S. Kuutti, R. Bowden, Y. Jin, P. Barber et al., "A Survey of Deep Learning Applications to Autonomous Vehicle Control," 2019. [PDF]

S. Malik, M. Ahmed Khan, H. El-Sayed, J. Khan et al., "How Do Autonomous Vehicles Decide?," 2022. ncbi.nlm.nih.gov

J. Azimjonov, A. Özmen, and M. Varan, "A vision-based real-time traffic flow monitoring system for road intersections," 2023. ncbi.nlm.nih.gov

X. Wang, "Vehicle Image Detection Method Using Deep Learning in UAV Video," 2022. ncbi.nlm.nih.gov

H. Ye, L. Liang, G. Ye Li, J. B. Kim et al., "Machine Learning for Vehicular Networks," 2017. [PDF]

J. Felipe Restrepo-Arias, J. William Branch-Bedoya, J. Andres Zapata-Cortes, E. Giovanny Paipa-Sanabria et al., "Industry 4.0 Technologies Applied to Inland Waterway Transport: Systematic Literature Review," 2022. ncbi.nlm.nih.gov

B. Neupane, T. Horanont, and J. Aryal, "Real-Time Vehicle Classification and Tracking Using a Transfer Learning-Improved Deep Learning Network," 2022. ncbi.nlm.nih.gov

Y. Yang, T. Dwyer, K. Marriott, B. Jenny et al., "Tilt Map: Interactive Transitions Between Choropleth Map, Prism Map and Bar Chart in Immersive Environments," 2020. [PDF]

A. Alhilal, B. Finley, T. Braud, D. Su et al., "Distributed Vehicular Computing at the Dawn of 5G: a Survey," 2020. [PDF]

L. Mora, X. Wu, and A. Panori, "Mind the gap: Developments in autonomous driving research and the sustainability challenge," 2020. ncbi.nlm.nih.gov

A. Razi, X. Chen, H. Li, H. Wang et al., "Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review," 2022. [PDF]

L. Zhang, X. Yu, A. Daud, A. Rashid Mussah et al., "Application of 2D Homography for High Resolution Traffic Data Collection using CCTV Cameras," 2024. [PDF]

M. Tiezzi, S. Melacci, M. Maggini, and A. Frosini, "Video Surveillance of Highway Traffic Events by Deep Learning Architectures," 2019. [PDF]

Downloads

Download data is not yet available.