基于深度学习的多人驾驶行为自动驾驶车辆仿真

Husnul Khotami Kindi, Nungki Selviandro, Gia Wulandari
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引用次数: 0

摘要

近年来,自动驾驶汽车(AV)技术的进步使得这个话题很受欢迎,大大小小的公司都开始开发这种自动驾驶技术。除了大公司,一些研究人员也对开发这项技术感兴趣。然而,由于成本限制和安全问题,研究人员使用计算机模拟方法开发了自动驾驶汽车。本文的主要目的是创建一个仿真(AV)。模拟是使用Unity 3D中的Udacity自动驾驶汽车创建的。我们采取的第一步是在模拟中通过手动驾驶汽车来获取许多参与者的图像形式的数据集,以获得人类驾驶行为。获取数据集后,利用卷积神经网络算法的深度学习方法进行AV模型的形成过程。在本研究中,成功地进行了良好的自动驾驶仿真,汽车可以完美地沿着赛道行驶,不会发生碰撞或脱轨。从所进行的测试结果来看,所构建的模型得到了相当好的结果,其中准确率为71%,损失为0.0165。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
AUTONOMOUS VEHICLE SIMULATION WITH MULTI HUMAN DRIVING BEHAVIOR USING DEEP LEARNING
Advances in Autonomous Vehicle (AV) technology have made this topic popular in recent years, both large and small companies have started to develop this AV technology. Apart from large companies, several researchers are also interested in developing this technology. However, due to cost constraints and security issues, the researchers developed AV using a computer simulation approach. The main objective of this paper is to create a simulation (AV). The simulation was created using Udacity self-driving-car from Unity 3D. The first step we took was to take a dataset in the form of images from a number of participants by manually driving a car in a simulation to get Human-driving-behavior. After the dataset is obtained, the AV model formation process will then be carried out using the deep learning method of the Convolutional Neural Network algorithm. In this research, a good AV simulation has been successfully made, the car can run perfectly following the track without experiencing a collision or going off the track. From the results of the testing carried out, the model that was built got pretty good results where the accuracy was 71% and the loss was 0.0165.
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