Vol. 4 No. 2 (2024): African Journal of Artificial Intelligence and Sustainable Development
Articles

Machine Learning for Autonomous Driving Decision-Making

Dr. David Kim
Associate Professor of Cybersecurity, Kookmin University, South Korea

Published 23-10-2024

Keywords

  • Machine Learning,
  • Autonomous Driving,
  • Decision-Making

How to Cite

[1]
D. D. Kim, “Machine Learning for Autonomous Driving Decision-Making”, African J. of Artificial Int. and Sust. Dev., vol. 4, no. 2, pp. 93–107, Oct. 2024, Accessed: Nov. 14, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/196

Abstract

Autonomous driving is a collection of technologies that allow a vehicle to sense and perceive the physical and social environment, model and plan its driving behavior, and then make decisions based on a cost versus benefit analysis. The promise of autonomous driving is to create a safer system, with less congestion and a better user experience, that can operate at scale without significant manual intervention.

The definition of five levels of vehicle autonomy ranges from 0 to 5. Vehicles with level 0 autonomy have no automation and drivers execute all vehicle control tasks. With increased system capability, level 1 vehicles have some driver assist features while level 2 vehicles can autonomously control speed and steering but require driver attention. At levels 3, 4, and 5, vehicles operate with increasing autonomy and decreasing reliance on driver attention and intervention. Machine learning has been a part of the intelligent systems used in autonomous driving for decades. The rise in the use of machine learning is directly associated with the ability to capture, store, and process the vast amount of data generated by these highly automated vehicles.

Downloads

Download data is not yet available.

References

  1. 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.
  2. 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.
  3. 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.
  4. Singh, Jaswinder. "Sensor-Based Personal Data Collection in the Digital Age: Exploring Privacy Implications, AI-Driven Analytics, and Security Challenges in IoT and Wearable Devices." Distributed Learning and Broad Applications in Scientific Research 5 (2019): 785-809.
  5. 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.
  6. 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.
  7. 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.
  8. Singh, Jaswinder. "Social Data Engineering: Leveraging User-Generated Content for Advanced Decision-Making and Predictive Analytics in Business and Public Policy." Distributed Learning and Broad Applications in Scientific Research 6 (2020): 392-418.
  9. S. Kumari, “Real-Time AI-Driven Cybersecurity for Cloud Transformation: Automating Compliance and Threat Mitigation in a Multi-Cloud Ecosystem ”, IoT and Edge Comp. J, vol. 4, no. 1, pp. 49–74, Jun. 2024
  10. 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.