A Novel Machine Learning based Driving Decision Strategy

For determining the driving strategy, modern cars are solely dependent on different factors such as people and road conditions. In particular, in the case of self-driving cars that travel autonomously towards the destination, it needs to perform various repetitive identification, judgement, and cont...

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Bibliographic Details
Main Authors: Sahithi, P, Sravanthi, V, Vanetha, V.G, Krishna Reddy, D
Format: Conference Proceeding
Language:English
Subjects:
Gradient Boosting
Licenses
Machine Learning
Machine learning algorithms
MLP (Multilayer Perceptron)
Nonhomogeneous media
Prediction algorithms
Real-time systems
Roads
Rotation per Minute
Virtual environments
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Summary:For determining the driving strategy, modern cars are solely dependent on different factors such as people and road conditions. In particular, in the case of self-driving cars that travel autonomously towards the destination, it needs to perform various repetitive identification, judgement, and control operations. To address this challenges, this research study has designed and developed an advanced driving decision strategy by assessing both its internal and exterior components (consumable conditions, Rotation Per Minute levels, and so on) with the help of machine learning models. This will reduce in-vehicle computing and achieve the most effective decision by utilizing evolutionary algorithms, past storage data and huge real-time data generated while driving a car. The data saved in the cloud are assessed by the machine learning algorithms, which then select the best driving strategy. To perform these operations, this study uses gradient boost algorithm. The research result shows that the vehicle features have a substantial influence in making a driving decision.
ISSN:2768-5330
DOI:10.1109/ICICCS56967.2023.10142642