Abstract
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.
Highlights
Microscopic traffic simulation is widely used in research due to its high efficiency and low cost when comparing to the actual implementation in the real world
This paper provides a method of establishing a vehicle guidance model with different degrees of automation for representing the automation level of the present, near and the more distant future
This paper presents a microscopic vehicle guidance model with three different degrees
Summary
Microscopic traffic simulation is widely used in research due to its high efficiency and low cost when comparing to the actual implementation in the real world. In order to reproduce the driving behavior of human drivers, many driver models (driver–vehicle unit model) have been developed in microscopic traffic simulation. With more parameters, including vehicle gap, velocity difference, desired deceleration, etc These car-following models are widely used in traffic simulation nowadays. Naranjo [12] asset up an adaptive control system (ACC) based on a throttle and brake fuzzy control system It made vehicles behave human-like in car-following situations and open to cooperate with drivers. Chen et al [14] proposed an adaptive speed control method for a robot driver based on fuzzy logic and conducted driving experiments in a Ford Focus car. All parameters in the vehicle model the simulation results of the driver model and human driver data will be more convincing. Used in simulation were set according to the driving simulator so that the comparison of the simulation results of the driver model and human driver data will be more convincing
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