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Localization for Autonomous Vehicles Based on Deep Learning Network


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DOI: https://doi.org/10.15866/iree.v18i2.22581

Abstract


Autonomous Vehicles (AV) should know their location. This is localization. Inaccurate GPS coordinates are a barrier for cars trying to locate themselves. This paper introduces a deep neural network as a potential solution for autonomous localization in clear sunny urban driving scenarios. AlexNet is modified by lowering the number of layers to provide a precise AV position. This uses an RGB camera sensor, eliminating the need for pricey Light Detection And Ranging (LIDAR) or Radio Detecting And Ranging (RADAR) sensors. The contribution of this work will obtain high accuracy for AV localization and reduce error in predicted positions with short training time due to the light, accurate, and robust proposed network. For training CNN Convolutional Neural Network (CNN), camera Red Green Blue (RGB) picture inputs feed into it, producing location prediction as its output. In order to increase accuracy in training RGB images, the images are combined with depth images generated by the Intensity Hue Saturation (HIS) algorithm for testing CNN. Finally, the K-Mean algorithm is implemented to find the similarity of a given input image with street images to retrieve the location coordinates accurately. The acquired simulation results have showed the efficacy of the proposed approach with a 95.49 % accuracy rate. The constructed network's test results have showed the most significant improvement compared to the previous related works.
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Keywords


Autonomous Vehicle; Convolutional Neural Networks CNN; Deep Learning; Intensity Hue Saturation IHS; K-mean Algorithm; Localization

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References


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