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

Machine Learning for Autonomous Vehicle Lane Change Prediction and Execution

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  • Dr. Fei-Fei Li
Dr. Fei-Fei Li
Professor of Computer Science, Stanford University (Branch outside normal colleges)
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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. 07, 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.

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