The article outlines various approaches to developing a fuzzy decision algorithm designed for monitoring and issuing warnings about driver drowsiness. This algorithm is based on analyzing EOG (electrooculography) signals and eye state images with the aim of preventing accidents. The drowsiness warning system comprises key components that learn about, analyze and make decisions regarding the driver’s alertness status. The outcomes of this analysis can then trigger warnings if the driver is identified as being in a drowsy state. Driver drowsiness is characterized by a gradual decline in attention to the road and traffic, diminishing driving skills and an increase in reaction time, all contributing to a higher risk of accidents. In cases where the driver does not respond to the warnings, the ADAS (advanced driver assistance systems) system should intervene, assuming control of the vehicle’s commands.

In the context in which most industries focus on digitalization and automation of production processes and on developing new products, the car manufacturing industry is advancing significantly. The automotive industry together with the IT field, are at an advanced stage in terms of the structure of autonomous vehicles, developing new technologies. The objective of this paper is to present the current state of technology regarding autonomous vehicles. Aspects regarding the devices and technologies used in the construction of these vehicles are approached, but there is also information regarding the way of classifying them according to the driver’s assistance level. In addition, some information about the evolution of automated vehicle development is presented.

A virtual model enables the study of reality in a virtual environment using a theoretical model, which is a digital image of a real model. The complexity of the virtual model must correspond to the reality of the evaluated system, becoming as complex as necessary and nevertheless as simple as possible, allowing for computer simulation results to be validated by experimental measurements. The virtual model of the autonomous vehicle was created using the CarMaker software package version 12.0, which was developed by the IPG Automotive company and is extensively used in both the international academic community and the automotive industry. The virtual model simulates the real-time operation of a vehicle’s elementary systems at the system level and provides an open platform for the development of virtual test scenarios in the application areas of autonomous vehicles, ADAS, Powertrain, and vehicle dynamics. This model included the following virtual sensors: slip angle sensor, inertial sensor, object sensor, free space sensor, traffic sign sensor, line sensor, road sensor, object-by-line sensor, camera sensor, global navigation sensor, radar sensor, lidar sensor, and ultrasonic sensor. Virtual sensors can be classified based on how they generate responses: sensors that operate on parameters derived from measurement characteristics, sensors that operate on developed modeling methods, and sensors that operate on applications.

The reduction in environment pollutant emissions is one of the main challenges regarding ground transportation. Internal combustion engines, used especially in hybrid propulsion systems, may be a solution in the transition to fully electric cars. Therefore, more efficient engines in terms of fuel consumption, emission generation and power density must be developed. This paper presents research regarding the architecture of the combustion chamber of an internal combustion engine with opposed pistons. The aim of this research was to find a combustion chamber architecture that would enable the engine to perform close to the program target of: NOx < 3.5 g/kWh, smoke (FSN) < 1, specific fuel consumption (bsfc) < 198 g/kWh. Three variants of the combustion chamber’s architecture have been studied. After the experimental research, the conclusion was that none of them fully reached the target; however, significant improvements have been achieved compared with the starting point. As a result, further research needs to be carried out in order to reach and even exceed the target.

Road transport is an important factor in carbon dioxide emissions. These emissions can be reduced by improving propulsion sources and traffic flow (avoiding traffic jams due to accidents). This article presents a system for monitoring and warning the driver to prevent a possible accident involving material damage, injury, or loss of life. The system performs video monitoring of the driver in order to determine his state (tired or attentive). By reducing traffic incidents and traffic jams, the energy consumed will not be wasted; thus, more sustainable transport energy use can be achieved.

A virtual model enables the study of reality in a virtual environment using a theoretical model, which is a digital image of a real model. The complexity of the virtual model must correspond to the reality of the evaluated system, becoming as complex as necessary nevertheless as simple as possible, allowing for computer simulation results to be vali-dated by experimental measurements. The virtual model of the autonomous vehicle was created using the CarMaker software package, which was developed by the IPG Auto-motive company and is extensively used in both the international academic community and the automotive industry. The virtual model simulates the real-time operation of a vehicle's elementary systems at the system level and provides an open platform for the development of virtual test scenarios in the Autonomous Vehicles, ADAS, Powertrain, and Vehicle Dynamics application areas. This model included the following virtual sen-sors: slip angle sensor, inertial sensor, object sensor, free space sensor, traffic sign sensor, line sensor, road sensor, object by line sensor, camera sensor, global navigation sensor, radar sensor, lidar sensor and ultrasonic sensor. Virtual sensors can be classified based on how they generate responses: sensors that operate on parameters derived from measurement characteristics, sensors that operate on developed modeling methods, and sensors that operate on applications.