LaksNet：一种用于Udacity模拟器中自动驾驶汽车的端到端深度学习模型
LaksNet: an end-to-end deep learning model for self-driving cars in Udacity simulator
AI总结：
本文提出了一种名为LaksNet的端到端卷积神经网络模型，包含四个卷积层和两个全连接层，用于Udacity模拟器中的自动驾驶汽车，实验表明其不驶离轨道的持续时间优于现有预训练ImageNet和NVIDIA模型。
摘要：
大多数道路交通事故的发生是由于人为错误，包括分心、鲁莽和酒后驾驶。克服这种危险情况的有效方法之一是在车辆中实现自动驾驶技术。在本文中，我们专注于为自动驾驶汽车构建一个高效的深度学习模型。我们提出了一种名为`LaksNet'的新型有效的卷积神经网络模型，该模型由四个卷积层和两个全连接层组成。我们使用从Udacity模拟器生成的训练数据，对我们的LaksNet模型进行了大量实验。在汽车于模拟器上行驶而不驶离轨道的持续时间方面，我们的模型优于许多现有的预训练ImageNet和NVIDIA模型。
英文摘要：
The majority of road accidents occur because of human errors, including distraction, recklessness, and drunken driving. One of the effective ways to overcome this dangerous situation is by implementing self-driving technologies in vehicles. In this paper, we focus on building an efficient deep-learning model for self-driving cars. We propose a new and effective convolutional neural network model called `LaksNet' consisting of four convolutional layers and two fully connected layers. We conduct extensive experiments using our LaksNet model with the training data generated from the Udacity simulator. Our model outperforms many existing pre-trained ImageNet and NVIDIA models in terms of the duration of the car for which it drives without going off the track on the simulator.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
