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- W4387349110 abstract "This paper presents a novel CNN architecture using an end-to-end learning technique to predict the steering angle for self-driving cars. The front camera is the only sensor used to generate this control command. This network was trained and tested on Sully Chen Public Dataset, which contains image frames and steering angle data for each image. The test outcomes demonstrated that this model could generate a reasonably accurate steering angle for autonomous vehicles and perform about 60% better than the networks designed from 2017 to 2021. In addition, the problem of overfitting in previous networks has mainly been addressed with the help of the new network architecture and different data preprocessing." @default.
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- W4387349110 date "2023-01-01" @default.
- W4387349110 modified "2023-10-06" @default.
- W4387349110 title "Generating Control Command for an Autonomous Vehicle Based on Environmental Information" @default.
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- W4387349110 doi "https://doi.org/10.1007/978-3-031-43763-2_12" @default.
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