Vol. 3 No. 2 (2023): African Journal of Artificial Intelligence and Sustainable Development
Articles

Machine Learning for Autonomous Vehicle Lane Change Prediction and Execution

PDF
  • Dr. Fei-Fei Li
Dr. Fei-Fei Li
Professor of Computer Science, Stanford University (Branch outside normal colleges)
Cover

Published 14-09-2023

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Dr. Fei-Fei Li, “Machine Learning for Autonomous Vehicle Lane Change Prediction and Execution”, African J. of Artificial Int. and Sust. Dev., vol. 3, no. 2, pp. 153–175, Sep. 2023, Accessed: Nov. 22, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/91

Abstract

Predicting whether vehicles in the front or rear intend to change their lanes is advantageous for driving safely and smoothly on expressways and other roads where lane changes are frequent [1]. Many methods have been developed to predict such behavior, including trajectory analysis, steering wheel extraction, and extraction from images of vehicle-mounted cameras or sensors. The most recent automatic lane-changing prediction models use visual information, such as the lane-changing decision or the intention of surrounding vehicles (optical flow), but these scenarios are very difficult because the direct observation of another vehicle lane-changing decision is not available. Moreover, GANs have been widely used to learn the mapping relationship between examples in various applications.

PDF

Downloads

Download data is not yet available.

References

  1. Tatineni, S., and A. Katari. “Advanced AI-Driven Techniques for Integrating DevOps and MLOps: Enhancing Continuous Integration, Deployment, and Monitoring in Machine Learning Projects”. Journal of Science & Technology, vol. 2, no. 2, July 2021, pp. 68-98, https://thesciencebrigade.com/jst/article/view/243.
  2. Shahane, Vishal. "Evolving Data Durability in Cloud Storage: A Historical Analysis and Future Directions." Journal of Science & Technology 1.1 (2020): 108-130.
  3. Abouelyazid, Mahmoud. "Comparative Evaluation of VGG-16 and U-Net Architectures for Road Segmentation." Eigenpub Review of Science and Technology 6.1 (2022): 75-91.
  4. Prabhod, Kummaragunta Joel. "Advanced Techniques in Reinforcement Learning and Deep Learning for Autonomous Vehicle Navigation: Integrating Large Language Models for Real-Time Decision Making." Journal of AI-Assisted Scientific Discovery 3.1 (2023): 1-20.
  5. Tatineni, Sumanth, and Sandeep Chinamanagonda. “Leveraging Artificial Intelligence for Predictive Analytics in DevOps: Enhancing Continuous Integration and Continuous Deployment Pipelines for Optimal Performance”. Journal of Artificial Intelligence Research and Applications, vol. 1, no. 1, Feb. 2021, pp. 103-38, https://aimlstudies.co.uk/index.php/jaira/article/view/104.