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Deep Learning Technique-Based Steering of Autonomous Car

机译:基于深度学习技术的自主轿车转向

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摘要

Deep neural network (DNN) has many advantages. Autonomous driving has become a popular topic now. In this paper, an improved stack autoencoder based on the deep learning techniques is proposed to learn the driving characteristics of an autonomous car. These techniques realize the input data adjustment and solving diffusion gradient problem. A Raspberry Pi and a camera module are mounted on the top of the car. The camera module provides the images needed for training the DNN. There are two stages in the training. In the pre-training process, an improved autoencoder is trained by the unsupervised learning mechanism, and the characterization of the track is extracted. In the fine-tuning stage, the whole network is trained according to the labeled data, and then this model learns the driving characteristics better according to the samples. In the experimental stage, the car will predict the action of the car by the trained model in the autonomous mode. The experiment exhibits the effectiveness of the proposed model. Compared with the traditional neural network, the improved stack autoencoder has a better generalization ability and faster convergence speed.
机译:深度神经网络(DNN)具有许多优点。自主驾驶现在已成为一个热门话题。本文提出了一种基于深度学习技术的改进的堆栈自动化器,以了解自动轿厢的驱动特性。这些技术实现了输入数据调整和求解扩散梯度问题。覆盆子PI和相机模块安装在汽车顶部。相机模块提供培训DNN所需的图像。训练中有两个阶段。在预训练过程中,通过无监督的学习机制训练改进的AutoEncoder,提取了轨道的表征。在微调阶段,整个网络根据标记的数据培训,然后根据样品更好地学习驱动特性。在实验阶段,汽车将通过训练模型在自主模式下预测汽车的动作。实验表现出所提出的模型的有效性。与传统的神经网络相比,改进的堆栈自动化器具有更好的泛化能力和更快的收敛速度。

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