Autonomous driving is one of the most recent topics of interest which is aimed at replicating human driving behavior keeping in mind the safety issues. We approach the problem of learning synthetic driving using generative neural networks. The main idea is to make a controller trainer network using images plus key press data to mimic human learning. We used the architecture of a stable GAN to m...

With the energy consumption rising in rail transport, the railway sector is showing increasing interest in the energy-efficient operation of freight trains. Freight trains require more complicated driving strategies than ordinary passenger trains do due to their heavy loads, especially in the long-distance steep downhill (LDSD) sections that are very common in freight rail lines in China. This ...

conclusions despite relatively high prevalence of smoking than drug use in zahedan drivers, there are few reports of these behaviors. laboratory tests cannot suitably measure substance use due to their interference with other substances. considering adverse effects of smoking on health and driving, it is necessary to train drivers to reduce or stop smoking during driving. results generally, fou...

Traditional control algorithms in Automatic Train Operation (ATO) system have some drawbacks, such as high energy consumption and low comfort. In this paper, two Intelligent Train Operation (ITO) models based on data mining for the subway train control are proposed combining with the driving experience. Firstly the training data set is sorted out and sieved out from the real train operation dat...

The accuracy in determined energy consumption and running time of trains, by means of computer simulation, is dependent upon the various models used. This thesis aims at developing validated models of running resistance, train and of a general driver, all based on full scale testing. A partly new simple methodology for determining running resistance, called by energy coasting method is develope...