Designing a Methodology to Calibrate and Validate Virtual Test Driving in a Large-Scale Network with Multisource Data
Year: 2022
Type: article
Abstract: Virtual test driving is a methodology to evaluate vehicle performance and environmental impacts under realistic driving conditions. However, it is challenging to ensure a sufficient correlation between virtual and various real-life traffic situations.In this study, a microscopic traffic simulation models a large-scale traffic network in the district of Aachen, and a drivetrain model represents the longitudinal dynamics of a real car. A methodology was developed to calibrate and validate both simulation models using field data from multiple sources. The comparison between the field data and simulation is threefold: we used the traffic counts to evaluate the simulated traffic volume at the macroscopic level, the drone-captured trajectory data to characterize the individual vehicle movements on highways, roundabouts, and intersections, and the real driving data of probe vehicles to investigate the influence of vehicle trajectories on energy demand.We also developed a generic approach to investigate the correlation between driving cycles and drivetrain performance, accounting for different combinations of real and virtual objects in three calibration and validation stages. Furthermore, the concept of mapping jigsaw puzzle scenarios was proposed to replicate the drone-captured concrete scenarios in the simulation and extend the calibrated results to a large-scale network. The results indicate that the proposed toolchain can effectively cope with the validation complexity of virtual test driving. The statistical analysis reveals the benefits of virtual test driving in providing a wide spectrum of realistic driving conditions in a cost-efficient and time-saving manner.
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4300579261