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- W2901652009 abstract "Fully automatic parking (FAP) is a key step towards the age of autonomous vehicle. Motivated by the contribution of human vision to human parking, in this paper, we propose a computer vision based FAP method for the autonomous vehicles. Based on the input images from a rear camera on the vehicle, a convolutional neural network (CNN) is trained to automatically output the steering and velocity commands for the vehicle controlling. The CNN is trained by Caffe deep learning framework. A 1/10th autonomous vehicle research platform (1/10-SAVRP), which configured with a vehicle controller unit, an automated driving processor, and a rear camera, is used for demonstrating the parking maneuver. The experimental results suggested that the proposed approach enabled the vehicle to gain the ability of parking independently without human input in different driving settings." @default.
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- W2901652009 date "2018-09-30" @default.
- W2901652009 modified "2023-09-23" @default.
- W2901652009 title "An End-to-End Fully Automatic Bay Parking Approach for Autonomous Vehicles" @default.
- W2901652009 doi "https://doi.org/10.1115/dscc2018-9126" @default.
- W2901652009 hasPublicationYear "2018" @default.
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