Real-Time AI-Based Solutions for Vehicle Collision Avoidance

Authors

  • Dr. Gabriela Gómez-Marín Professor of Industrial Engineering, National University of Colombia Author

Keywords:

AI-Based Solutions, Vehicle Collision Avoidance

Abstract

Today, the most talked about issue in vehicle safety is collision avoidance. Every day, the number of vehicles on the road increases, and with it comes an increase in the potential for collision accidents. This increase presents a compelling reason to develop innovative technologies to keep both drivers and passengers safe. Artificial Intelligence has undergone rapid advancement over the last decade and can efficiently solve complex problems, including designing a modern automotive system. Through considerable research, AI can be effectively integrated into conventional automotive hardware and used to deliver practical automotive solutions in real-time. In the past few years, significant improvements in the field of collision prevention have been made, but few present a complete comparison between their proposed system and others.

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References

Tamanampudi, Venkata Mohit. "Automating CI/CD Pipelines with Machine Learning Algorithms: Optimizing Build and Deployment Processes in DevOps Ecosystems." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 810-849.

Pal, Dheeraj Kumar Dukhiram, et al. "AI-Assisted Project Management: Enhancing Decision-Making and Forecasting." Journal of Artificial Intelligence Research 3.2 (2023): 146-171.

Kodete, Chandra Shikhi, et al. "Determining the efficacy of machine learning strategies in quelling cyber security threats: Evidence from selected literatures." Asian Journal of Research in Computer Science 17.8 (2024): 24-33.

Singh, Jaswinder. "The Rise of Synthetic Data: Enhancing AI and Machine Learning Model Training to Address Data Scarcity and Mitigate Privacy Risks." Journal of Artificial Intelligence Research and Applications 1.2 (2021): 292-332.

Alluri, Venkat Rama Raju, et al. "Serverless Computing for DevOps: Practical Use Cases and Performance Analysis." Distributed Learning and Broad Applications in Scientific Research 4 (2018): 158-180.

Machireddy, Jeshwanth Reddy. "Revolutionizing Claims Processing in the Healthcare Industry: The Expanding Role of Automation and AI." Hong Kong Journal of AI and Medicine 2.1 (2022): 10-36.

Tamanampudi, Venkata Mohit. "Autonomous AI Agents for Continuous Deployment Pipelines: Using Machine Learning for Automated Code Testing and Release Management in DevOps." Australian Journal of Machine Learning Research & Applications 3.1 (2023): 557-600.

J. Singh, “How RAG Models are Revolutionizing Question-Answering Systems: Advancing Healthcare, Legal, and Customer Support Domains”, Distrib Learn Broad Appl Sci Res, vol. 5, pp. 850–866, Jul. 2019

S. Kumari, “AI-Enhanced Mobile Platform Optimization: Leveraging Machine Learning for Predictive Maintenance, Performance Tuning, and Security Hardening ”, Cybersecurity & Net. Def. Research, vol. 4, no. 1, pp. 29–49, Aug. 2024

Tamanampudi, Venkata Mohit. "Leveraging Machine Learning for Dynamic Resource Allocation in DevOps: A Scalable Approach to Managing Microservices Architectures." Journal of Science & Technology 1.1 (2020): 709-748.

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Published

04-11-2024

How to Cite

[1]
D. G. Gómez-Marín, “Real-Time AI-Based Solutions for Vehicle Collision Avoidance”, J. of Artificial Int. Research and App., vol. 4, no. 2, pp. 109–124, Nov. 2024, Accessed: Dec. 04, 2024. [Online]. Available: https://aimlstudies.co.uk/index.php/jaira/article/view/284

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