The ever-increasing number of novel functions in modern vehicles continuously expands with the application of cognitive information technology, creating a new need for testing during market introduction. As the virtual test environment evolves, the need for real tests conducted on the road continuously decreases, saving time and cost while maximizing quality indicators. This article presents a new type of automatic monitoring system created in a fully virtual test environment. The automated assessment during dynamometer testing (ADT) method automatically evaluates the values measured on the engine dynamometer at predefined intervals, compares them to reference data, and provides feedback on the correctness of the current test. The present paper discusses the monitoring methodology and its application on an engine dynamometer, and it presents the results of the method applied during a real engine test.

— In connection with road vehicles and their internal combustion engines, their effects on our environment are being dealt with more and more. Plenty of parameters could be listed, but human catabolism and combustion of liquid fuels probably have not been examined together. Carbon dioxide has the most priority as a greenhouse gas in environmental change and metrology, thus it is a constant topic. Oxygen consumption has been examined rarely or never in such a context. In this article, calculations have been carried out from different points of view regarding these two parameters. The results of total-quantity calculations show, that the oxygen demand for the combustion of fuels used for road transport in 2019 in Hungary is the same as the 6-year oxygen demand of the Hungarian population, and the amount of the carbon dioxide emitted by the combustion of fuel used in road transport in Hungary is the same as the amount emitted by the Hungarian population during 5.2 years. The results might be worth examining on a larger scale.

The development of vehicle simulation models is becoming increasingly important, especially for the virtual validation of new automotive features. New methods are emerging that need to be tested and validated to ensure their proper functioning. The use of artificial intelligence methods can accelerate product development processes, so it is essential for companies to examine the applicability of these solutions. This article compares the results of a traditional rule-based transmission model and an artificial intelligence-based transmission model against real vehicle measurements. Creating a transmission model using the new artificial intelligence-based method requires less time and effort than developing a rule-based model. However, experimental data is required to train the model. This paper addresses the challenges of artificial intelligence-based transmission modeling and highlights the new advantages of artificial intelligence-based solutions.