Intelligent vehicles gradually enter the vehicular fleet with advanced driver-assistance technologies. Their impact on traffic should, therefore, be considered by transportation decision-makers. This paper examines the effect of vehicles with different levels of automation on traffic flow, such as non-assisted vehicles, vehicles with driver assistance systems, and fully autonomous vehicles. The accuracy of the examined traffic scenario is also an important factor in microscopic traffic simulation. In this paper, the central part of the city of Duisburg, Duisburg’s inner ring, is chosen for the traffic scenario. Through the cooperation with local government, official data of Origin/Destination matrices, induction loops, and traffic light plans are provided for this work. Thus, traffic demand from Origin/Destination matrices and induction loops are generated and compared, respectively. Finally, vehicles with different levels of automation are simulated in the Duisburg inner ring scenario.
This paper presents a microscopic vehicle guidance model which adapts to different levels of vehicle automation. Independent of the vehicle, the driver model built is different from the common microscopic simulation models that regard the driver and the vehicle as a unit. The term “Vehicle Guidance Model” covers, here, both the human driver as well as a combination of human driver and driver assistance system up to fully autonomously operated vehicles without a (human) driver. Therefore, the vehicle guidance model can be combined with different kinds of vehicle models. As a result, the combination of different types of driver (human/machine) and different types of vehicle (internal combustion engine/electric) can be simulated. Mainly two parts constitute the vehicle guidance model in this paper: the first part is a traditional microscopic car-following model adjusted according to different degrees of automation level. The adjusted model represents the automation level for the present and the near and the more distant future. The second part is a fuzzy control model that describes how humans adjust the pedal position when they want to reach a target speed with their vehicle. An experiment with 34 subjects was carried out with a driving simulator based on the experimental data and the fuzzy control strategy was determined. Finally, when comparing the simulated model data and actual driving data, it is found that the fuzzy model for the human driver can reproduce the behavior of human participants almost accurately.
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