This research work is aimed directly at the study of network traversal, for the design of vehicle dynamics and driving test programs. The work can be applied more widely to the qualification of test tracks. In addition to general modelling, this method can also be used to investigate the formation of loops in order to take into account, the sub-routes, multiple times. An important area of its application is the more comprehensive analysis of complex loads, as well as, the development of learning algorithms, which can be achieved by repeating multiple traversals of certain track sections within a series of measurements, and can be used for the development of on-board vehicle systems. The mathematical modelling is presented through the application of the geometric graph and subgraphs of the track. The properties of the Markov model extracted from the connection matrix of the large-scale network model are also presented. In this way, the modelling is extended to the application of the connection matrix of the large-scale network model as well. The modelling and computational details are demonstrated by means of a computer based, algebraic program. This modelling and the results of the calculations, will allow the further development of a test program design and related evaluation methods.

The aim of the research is to develop a laboratory model-based diagnostic procedure that performs tests of the motion processes of autonomous electric vehicles in a particular city, on a transport network or track. The test consists of a laboratory based generation of the corresponding speed and steering angle signals, being in accordance with real driving and traffic conditions, which are also used in the test procedure. The procedure takes into account the real trajectory tracking process as well (Péter and Lakatos, 2017).

The research identifies the dynamical parameters in the area of mechanics within the base of traffic model parameters used by the Intelligent Driver Model (IDM), which are the highest acceleration parameters set by the vehicle, the desired speed parameters of the vehicle and the distance-keeping parameters of the vehicle. All this facilitates the automatic control of autonomous electric vehicles in certain vehicle groups.