Machine Learning for Enhancing Autonomous Vehicle Decision-Making in Urban Environments
Keywords:
Machine Learning, Autonomous Vehicle Decision-MakingAbstract
An autonomous vehicle (AV) can be defined as a self-driving or robotic vehicle that is capable of traveling without human command. These vehicles are seen as an integral part of modern, smart cities. Although AV technology is not a new concept, it has only recently been integrated into urban infrastructures. From the development of partial automation, the first experiments with autonomous public transport in urban environments have been conducted to help last mile mobility solutions. This development has gained interest from companies and governments worldwide to continue this work in the hopes of eventually constructing fully connected, automated, smart cities. By doing so, a range of functions could aid and improve urban living by targeting urban mobility issues, as well as addressing other urban challenges like pollution and improving care services.
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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. "AIOps: Integrating AI and Machine Learning into IT Operations." Australian Journal of Machine Learning Research & Applications 4.1 (2024): 288-311.
Pasupuleti, Vikram, et al. "Enhancing supply chain agility and sustainability through machine learning: Optimization techniques for logistics and inventory management." Logistics 8.3 (2024): 73.
J. Singh, “Robust AI Algorithms for Autonomous Vehicle Perception: Fusing Sensor Data from Vision, LiDAR, and Radar for Enhanced Safety”, Journal of AI-Assisted Scientific Discovery, vol. 4, no. 1, pp. 118–157, Apr. 2024
Alluri, Venkat Rama Raju, et al. "DevOps Project Management: Aligning Development and Operations Teams." Journal of Science & Technology 1.1 (2020): 464-487.
Machireddy, Jeshwanth Reddy. "Assessing the Impact of Medicare Broker Commissions on Enrollment Trends and Consumer Costs: A Data-Driven Analysis." Journal of AI in Healthcare and Medicine 2.1 (2022): 501-518.
Ahmad, Tanzeem, et al. "Hybrid Project Management: Combining Agile and Traditional Approaches." Distributed Learning and Broad Applications in Scientific Research 4 (2018): 122-145.
Tamanampudi, Venkata Mohit. "AI-Powered NLP Agents in DevOps: Automating Log Analysis, Event Correlation, and Incident Response in Large-Scale Enterprise Systems." Journal of Artificial Intelligence Research and Applications 4.1 (2024): 646-689.
J. Singh, “The Ethical Implications of AI and RAG Models in Content Generation: Bias, Misinformation, and Privacy Concerns”, J. Sci. Tech., vol. 4, no. 1, pp. 156–170, Feb. 2023
S. Kumari, “Optimizing Mobile Platform Security with AI-Powered Real-Time Threat Intelligence: A Study on Leveraging Machine Learning for Enhancing Mobile Cybersecurity”, J. of Art. Int. Research, vol. 4, no. 1, pp. 332–355, Jan. 2024.
Praveen, S. Phani, et al. "Revolutionizing Healthcare: A Comprehensive Framework for Personalized IoT and Cloud Computing-Driven Healthcare Services with Smart Biometric Identity Management." Journal of Intelligent Systems & Internet of Things 13.1 (2024).
Bonam, Venkata Sri Manoj, et al. "Secure Multi-Party Computation for Privacy-Preserving Data Analytics in Cybersecurity." Cybersecurity and Network Defense Research 1.1 (2021): 20-38.
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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