: Autonomous cooperative driving systems require the integration of research activities in the field of embedded systems, robotics, communication, control and artificial intelligence in order to create a secure and intelligent autonomous drivers behaviour patterns in the traffic. Beside autonomous vehicle management, an important research focus is on the cooperation behaviour management. In this paper, we propose hybrid automaton modelling to emulate flexible vehicle Platoon and vehicles cooperation interactions. We introduce novel coding function for Platoon cooperation behaviour profile generation in time, which depends of vehicles number in Platoon and behaviour types. As the behaviour prediction of transportation systems, one of the primarily used methods of artificial intelligence in Intelligent Transport Systems, we propose an approach towards NARX neural network prediction of Platoon cooperation behaviour profile. With incorporation of Platoon manoeuvres dynamic prediction, which is capable of analysing traffic behaviour, this approach would be useful for secure implementation of real autonomous vehicles cooperation.

ABSTRACT The thermal power plant systems are one of the most complex dynamical systems which must function properly all the time with least amount of costs. More sophisticated monitoring systems with early detection of failures and abnormal behaviour of the power plants are required. The detection of anomalies in historical data using machine learning techniques can lead to system health monitoring. The goal of the research is to build a neural network-based data-driven model that will be used for anomaly detection in selected sections of thermal power plant. Selected sections are Steam Superheaters and Steam Drum. Inputs for neural networks are some of the most important process variables of these sections. All of the inputs are observable from installed monitoring system of thermal power plant, and their anomaly/normal behaviour is recognized by operator's experiences. The results of applying three different types of neural networks (MLP, recurrent and probabilistic) to solve the problem of anomaly detection confirm that neural network-based data-driven modelling has potential to be integrated in real-time health monitoring system of thermal power plant.

The aim of this paper is to demonstrate, how to communicate the vehicle between themselves in a heterogeneous vehicles network, and show on which way is done the exchange of information with the infrastructure, to overcome the shortcomings of using a single wireless technology. DSRC does not offer enough good coverage and range around intersections in urban areas for specific applications. On the other side, as an alternative to overcoming these deficiencies proposed LTE, advanced mobile communications technology. The evaluation of performance is usually done by means of simulations in particular the programming software, which integrates tools to support Wi-Fi, IEEE 802.11p, mobile technology and feedback mobility. For the realization of heterogeneous networks vehicle that has support for LTE is used open source simulator for communication between vehicles and infrastructure Veins LTE, composed of a network simulator Omnet++ and traffic simulator SUMO. These two simulators are working in parallel, and allow modeling of communication between vehicles.

To accurately predict traffic information is of great importance in a large number of applications in connection with Intelligent Transport systems (ITS), since it reduces the uncertainty of future traffic states and improves traffic mobility. The most important research is done in the domain of cooperative intelligent transport system (C-ITS). Only minor attention has been given to coordinated maneuvering, since testing with real vehicles which can drive autonomously requires a large-scale infrastructure with important security measures. In this paper, we propose hybrid automaton modelling in Matlab/Simulink/ Stateflow to emulate flexible platooning conditions, analysing how cooperation interactions can be accomplished using inter-vehicle communication and certain control of the vehicles. Such analysis reveals to be necessary in order to establish the improvement of traffic mobility in Intelligent Transportation Systems through cooperation behaviour profile prediction. This study presents an approach towards NARX neural network prediction of flexible Platooning maneuvers profile. In order to estimate prediction, MSE and R were utilized. The study results suggest that in the case of noise in test data, NARX neural network would be an efficient prediction tool, and useful for the prediction mobility in Intelligent Transport systems.

Driving simulators are used to analyze and validate the driver's behavior. These simulators are an essential tool in the research of human factor related to car driving. Advantages of using a driving simulator are safety (there are no traffic accidents during driving) and simple collecting of data related to the driver's behavior. The goal of this paper is development of an interactive driving simulator capable of simulating and testing driver's behavior based on lane changing with selected traffic conditions. The overall system is controlled and supervised using the Finite State Machine modeling approach realized in C# programming language, data input/output processing is implemented using the microcontroller user-based interface as well as the virtual driving scenarios is created using Microsoft XNA platform. The usage of video game technology is a central design principle of virtual driving scenarios. The concept of driver physical reaction testing using the software simulator based on configurable parameters is presented too.

Intelligent Transportation Systems rely on understanding, predicting and affecting the interactions between vehicles. The goal of this paper is to choose a small subset from the larger set so that the resulting regression model is simple, yet have good predictive ability for Vehicle agent speed relative to Vehicle intruder. The method of ANFIS (adaptive neuro fuzzy inference system) was applied to the data resulting from these measurements. The ANFIS process for variable selection was implemented in order to detect the predominant variables affecting the prediction of agent speed relative to intruder. This process includes several ways to discover a subset of the total set of recorded parameters, showing good predictive capability. The ANFIS network was used to perform a variable search. Then, it was used to determine how 9 parameters (Intruder Front sensors active (boolean), Intruder Rear sensors active (boolean), Agent Front sensors active (boolean), Agent Rear sensors active (boolean), RSSI signal intensity/strength (integer), Elapsed time (in seconds), Distance between Agent and Intruder (m), Angle of Agent relative to Intruder (angle between vehicles °), Altitude difference between Agent and Intruder (m)) influence prediction of agent speed relative to intruder. The results indicated that distance between Vehicle agent and Vehicle intruder (m) and angle of Vehicle agent relative to Vehicle Intruder (angle between vehicles °) is the most influential parameters to Vehicle agent speed relative to Vehicle intruder.

Roundabout intersections promote a continuous flow of traffic. Roundabouts entry move traffic through an intersection more quickly, and with less congestion on approaching roads. With the introduction of smart vehicles and cooperative decision-making, roundabout management shortens the waiting time and leads to a more efficient traffic without breaking the traffic laws and earning penalties. This paper proposes a novel approach of cooperative behavior strategy in conflict situations between the autonomous vehicles in roundabout using game theory. The game theory presents a strategic decision-making technique between independent agents - players. Each individual player tends to achieve best payoff, by analyzing possible actions of other players and their influence on game outcome. The Prisoner's Dilemma game strategy is selected as approach to autonomous vehicle-to-vehicle (V2V) decision making at roundabout test-bed, because the commonly known traffic laws dictate certain rules of vehicle's behavior at roundabout. It is shown that, by integrating non-zero-sum game theory in autonomous vehicle-to-vehicle (V2V) decision making capabilities, the roundabout entry problem can be solved efficiently with shortened waiting times for individual autonomous vehicles.

Accurate prediction of traffic information is important in many applications in relation to Intelligent Transport systems (ITS), since it reduces the uncertainty of future traffic states and improves traffic mobility. There is a lot of research done in the field of traffic information predictions such as speed, flow and travel time. The most important research was done in the domain of cooperative intelligent transport system (C-ITS). The goal of this paper is to introduce the novel cooperation behaviour profile prediction through the example of flexible Road Trains useful road cooperation parameter, which contributes to the improvement of traffic mobility in Intelligent Transportation Systems. This paper presents an approach towards the control and cooperation behaviour modelling of vehicles in the flexible Road Train based on hybrid automaton and neuro-fuzzy (ANFIS) prediction of cooperation profile of the flexible Road Train. Hybrid automaton takes into account complex dynamics of each vehicle as well as discrete cooperation approach. The ANFIS is a particular class of the ANN family with attractive estimation and learning potentials. In order to provide statistical analysis, RMSE (root mean square error), coefficient of determination (R2) and Pearson coefficient (r), were utilized. The study results suggest that ANFIS would be an efficient soft computing methodology, which could offer precise predictions of cooperative interactions between vehicles in Road Train, which is useful for prediction mobility in Intelligent Transport systems.

Regular and systematic public transport is of great importance to all residents in any country, in the city and on commuter routes. In our environment, users of public transport can track the movement of vehicles with great difficulty, given that the current system does not meet the necessary criteria, and does not comply with the functioning of transport system. The aim of the final paper is to show the development of such a system using ZigBee and Arduino platforms. This paper shows an example of use the technologies mentioned above, their main advantages and disadvantages, with the emphasis on communication between the device and its smooth progress. In order to show the way in which the system could function, a simple mesh network was created, consisting of coordinator, routers for data distribution and end devices representing the vehicles. To view the results a web application was developed using open-source tool which is for display of the collected data on the movement of nodes in the network.

Although wireless sensor networks (WSN) are mainly designed for gathering information and measuring environmental parameters, they can be effectively used to control and manage the electrical devices in the house. Thus, they become part of an intelligent and autonomous environments that can take part of the obligation of the tenant in the house. By combining the WSN with other networks such as the Internet or mobile phone network, ZigBee networks receive full functionality and the ability of remote monitoring and control. This paper describes an example of the design of wireless networks with Xbee module and its application in home use. The achieved tasks of the network that was implemented through this paper are: measuring the temperature of home area, automatic and manual control of individual actuator nodes and ZigBee network connectivity with other networks, storage and graphical representation of measured values.