International Journal For Multidisciplinary Research

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A Driving Decision Strategy Based On Machine Learning For An Autonomous Vehicle.

Author(s) Balaga Keertana, Kondru Supriya, Nadendla Pranay Kumar Chowdary, Imandi Gayathri, Kuppili Yasoda Krishna
Country India
Abstract The developing technology has made a big stage for several inventions or creations to be formed, worked on, functionalized and used for better improvement of human’s life. The idea of creating an autonomous vehicle is to improve the human driving skills by replacing human’s driving and using artificial
intelligence so that there is better usage of safety rules; avoid accidents, proper functioning of roads with well managed traffic and roads. A current autonomous vehicle determines its driving strategy by considering only external factors (Pedestrians, road conditions, etc.) without considering the interior condition of the vehicle. To solve the problem, this project proposes “A Driving Decision Strategy (DDS) Based on Machine learning for an autonomous vehicle” which determines the optimal strategy of an autonomous vehicle by analyzing not only the external factors, but also the internal factors of the vehicle (consumable conditions, RPM levels etc.). The DDS learns a genetic algorithm using sensor data from vehicles stored in the cloud and determines the optimal driving strategy of an autonomous vehicle. This project compared the DDS with MLP and RF neural network models to validate the DDS.
Keywords External Condition, Internal Condition, Driving Decision Strategy
Field Computer
Published In Volume 6, Issue 3, May-June 2024
Published On 2024-05-13
Cite This A Driving Decision Strategy Based On Machine Learning For An Autonomous Vehicle. - Balaga Keertana, Kondru Supriya, Nadendla Pranay Kumar Chowdary, Imandi Gayathri, Kuppili Yasoda Krishna - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.20168
DOI https://doi.org/10.36948/ijfmr.2024.v06i03.20168
Short DOI https://doi.org/gtt8tm
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