Securely connected vehicles - what it takes to make self-driving cars a reality

2016 ◽  
Author(s):  
Lars Reger
Keyword(s):  
Connected Vehicles ◽  
Self Driving Cars

Artificial Intelligence in Logistics

2019 ◽  
Vol 12 (1) ◽  
pp. 47-60
Author(s):  
László Kota
Keyword(s):  
Artificial Intelligence ◽  
Computing Power ◽  
Self Driving Cars

The artificial intelligence undergoes an enormous development since its appearance in the fifties. The computing power has grown exponentially since then, enabling the use of artificial intelligence applications in different areas. Since then, artificial intelligence applications are not only present in the industry, but they have slowly conquered households as well. Their use in logistics is becoming more and more widespread, just think of self-driving cars and trucks. In this paper, the author attempts to summarize and present the artificial intelligence logistical applications, its development and impact on logistics.

Fields of spatial research on urban areas in the context of organisational solutions to new forms of transportation

2018 ◽  
Vol 58 (1) ◽  
pp. 53-60
Author(s):  
Bartosz Czarnecki
Keyword(s):  
Urban Areas ◽  
Transportation System ◽  
Present Knowledge ◽  
Future Research ◽  
Use Of Self ◽  
Large Share ◽  
Main Achievement ◽  
Self Driving Cars

Abstract The paper discusses the spatial consequences of the widespread use of self-driving cars and the resulting changes in the structure of urban areas. Analysing present knowledge on the technology, functionality and future forms of organisation of mobility with this type of means of transportation, conclusions are presented concerning the expected changes in the organisation of space in urban areas. The main achievement of the investigation is an outline of the fields of future research on the spatial consequences of a transportation system with a large share of self-driving cars.

Cloning Safe Driving Behavior for Self-Driving Cars using Convolutional Neural Networks

2019 ◽  
Vol 12 (2) ◽  
pp. 120-127 ◽  
Author(s):  
Wael Farag
Keyword(s):  
Gradient Descent ◽  
Autonomous Driving ◽  
Driving Behavior ◽  
Training Data ◽  
Stochastic Gradient Descent ◽  
Data Set ◽  
Safe Driving ◽  
Processing Pipeline ◽  
Self Driving Cars ◽  
And Training

Background: In this paper, a Convolutional Neural Network (CNN) to learn safe driving behavior and smooth steering manoeuvring, is proposed as an empowerment of autonomous driving technologies. The training data is collected from a front-facing camera and the steering commands issued by an experienced driver driving in traffic as well as urban roads. Methods: This data is then used to train the proposed CNN to facilitate what it is called “Behavioral Cloning”. The proposed Behavior Cloning CNN is named as “BCNet”, and its deep seventeen-layer architecture has been selected after extensive trials. The BCNet got trained using Adam’s optimization algorithm as a variant of the Stochastic Gradient Descent (SGD) technique. Results: The paper goes through the development and training process in details and shows the image processing pipeline harnessed in the development. Conclusion: The proposed approach proved successful in cloning the driving behavior embedded in the training data set after extensive simulations.

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