自动驾驶汽车的模仿学习

Omer Qureshi, Muhammad Nouman Durrani, S. Raza
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引用次数: 0

摘要

世界正处于另一场工业革命的中期。但这一次,真正的革命将由全球的计算机科学家领导,而不是蒸汽机,他们将永远改变我们与环境互动的方式。在自动驾驶领域,有一小部分开创性的研究正在进行,以确保乘客的安全和人类的舒适。自动驾驶主要依赖于机器学习的子集,即模仿学习,几十年来一直是研究的主题。自动驾驶的关键问题是预测车辆的转向角度。行为克隆是模仿学习的一种形式,它从人类专家的行为中学习。然而,模仿学习有其自身的挑战,在某些条件下表现不佳。在本研究中,提出了一种新的预测车辆转向角的算法——CNNO。它有五个卷积层,两个最大池层,四个完全连接层,平坦层和一个退出层。随后将其与CNN- neural Circuit Policy、CNN、ResNet50、VGG16和VGG19架构进行比较。结果表明,该算法在第10、30和50 epoch的评价误差最好,在第70 epoch的训练误差最好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Imitation Learning for Autonomous Driving Cars
The world is the middle of another industrial revolution. But this time, instead of the steam engines, the real revolution will be led by computer scientists across the globe who will forever change the way we interact with our environment. A small subset of groundbreaking research has been going on in the field of autonomous driving to ensure the safety of passengers and human comfort. Autonomous driving, which primarily relies on the subset of machine learning i.e., imitation learning has been a subject of research for several decades now. The critical problem in autonomous driving is predicting the steering angles of the vehicle. Behavior cloning is a form of imitation learning and it learns from the actions of human experts. However, imitation learning has its own set of challenges and performs poorly in certain conditions. In this research a new algorithm is proposed, CNNO, to predict the steering angles of the vehicle. It has five convolution layers, two max pool layers, four fully connected layers, flatten layer, and a drop-out layer. It is subsequently compared against CNN-Neural Circuit Policy, CNN, ResNet50, VGG16, and VGG19 architectures. The proposed algorithm has shown to give the best evaluation error results from epochs 10, 30 & 50 and the best training error in epoch 70.
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