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- W3215719595 abstract "As of the present, completely autonomous vehicles are found to be functional only in zero to very less traffic conditions. In near future, self-driving vehicles will be frequently tested in urban traffic and will be able to coexist with human- driven vehicles. When an automated vehicle is employed in highway traffic, it has the need to understand the positions of its surrounding vehicles which share the same environment. It should have the ability to identify the lane in which the surrounding vehicles are moving. The paper intends to identify lane of the surrounding vehicles using several Neural Networks (NN) methods considering the public dataset from Next Generation Simulation (NGSIM) that provides real highway driving scenarios as input. The work is carried out in MATLAB using Machine Learning and Deep Learning tools of Neural Net Fitting and Classification Learner. The comparison of accuracy of different models are studied which can identify lane with significantly high accuracy up to 99.8%." @default.
- W3215719595 created "2021-12-06" @default.
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- W3215719595 date "2021-09-15" @default.
- W3215719595 modified "2023-09-27" @default.
- W3215719595 title "Lane Prediction by Autonomous Vehicle in Highway Traffic using Artificial Neural Networks" @default.
- W3215719595 doi "https://doi.org/10.1109/icecct52121.2021.9616881" @default.
- W3215719595 hasPublicationYear "2021" @default.
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