## 2 Literature Background
[19] determined fuel consumption and emissions of carbon monoxide (CO), NOx and PN for four spark-ignition, direct-ignition passenger cars and one hybrid electric vehicle while driving in real road traffic and in four type-approval driving tests: the Highway Fuel Economy Test (HWFET), the Urban Dynamometer Driving Schedule (UDDS), the Federal Test Procedure (FTP) and the New York City Cycle (NYCC). Likewise, [PERSON] et al. [20] compared pollutant emissions from a conventional vehicle with a spark-ignition engine and a hybrid vehicle of the same model series determined under Worldwide Harmonized Light Vehicles Test Cycles (WLTCs), the China light-duty vehicle test cycle (CLTC) and three different RDE test variants. However, all of the above were simulated on a chassis dynamometer for the reproducibility and comparability of the test results. Not only passenger cars, but also light commercial vehicles, buses and trucks are tested under real road traffic conditions. Extensive research results were presented by [PERSON] et al. [21]. They concerned the actual emission of pollutants from various types of vehicles, including eight light-duty passenger vehicles, hybrid electric vehicles, eight heavy-duty passenger vehicles and four light-duty trucks equipped with spark-ignition or compression-ignition engines. Some researchers analyze, in real-world driving conditions, the pollutant emissions and fuel consumption of motor vehicles with engines using unconventional fuels. [PERSON] et al. [22] tested a light-duty commercial vehicle supplied with bio-butanol and hydrotreated vegetable oil (HVO), both in the form of blends with diesel oil, in RDE using PEMS. The following pollutants were considered: CO, CO2, NOx, total hydrocarbons (THCs) and PN. [PERSON] et al. [23] devoted their research to the use of an innovative biofuel consisting of a mixture of renewable propane (bio-LPG) and renewable dimethyl ether (DME). Three vehicles with spark-ignition engines and a dual fuel system of biofuel and gasoline were used. The tests were conducted in accordance with the RDE procedure and--for comparison--the type-approval WLTC and research Common Artemis Driving Cycle (CADC). The pollutants examined were CO, NOx, THC, PN and greenhouse gases. Finally, there are publications that go beyond the scheme of direct presentation of research results obtained in road conditions, aiming at their deeper analysis, possibly towards the development of currently used methods. [PERSON] and [PERSON] [24] proposed a new method for obtaining driving tests, intended to be performed with chassis dynamometers, that mimics real-world driving scenarios occurring on the road. The foundation for developing these driving tests came from data collected using the RDE method. The key idea of the developed method was to determine probabilities of acceleration under certain conditions using logistic regression models that were trained on actual driving data. A paper by [PERSON] et al. [25] was concerned with the issue of using RDE data to calibrate vehicle drive systems. The authors suggested that the calibration methods used up to then had not sufficiently taken into account real driving conditions, as they had most often been optimized for specific homologation driving tests. In order to achieve increased powertrain reliability across various operating conditions, they proposed a probabilistic approach based on the stochastic optimization of pollutant emissions and fuel consumption derived from RDE tests. The above ideas refer to the concept presented in an earlier paper [26] by [PERSON] et al. that focused on using stochastic optimization for engine calibration with a diverse set of drive tests and Monte Carlo sampling to evaluate the quantities that needed optimization. The Monte Carlo method was also utilized in research work of [PERSON] et al., in [9], which aimed at the development of an algorithm for determining the characteristics of road emissions of pollutants, as well as road particle numbers and fuel consumption in the domain of the average vehicle velocity.