Validation Study of a Driving Simulation Platform

To validate the validity of a driving simulation platform, this study constructs a dual dataset using field data and driving simulation experiments on an urban arterial road. Comprehensive validation is conducted from both subjective perception and objective quantification perspectives. Subjectively, questionnaires assessed simulation fidelity, driving workload, and physiological symptoms. Objectively, five core car-following indicators (speed, acceleration, relative speed, headway, and time headway) were analyzed using dynamic time warping and statistical methods. The results demonstrate that the simulated scenarios exhibit high subjective fidelity, reasonable task loads, and controllable motion sickness risks. Objectively, dynamic time warping confirms strong temporal pattern similarity, with waveform consistency proportions across core indicators ranging from 91.67% to 100.0%. Macroscopically, satisfactory relative aggregate similarity is demonstrated, with relative difference between means consistently constrained within a 20% threshold. However, strict absolute behavioral validity is unsupported due to significant statistical differences in sequence means, establishing clear boundary constraints for trend replication. It is speculated that systematic biases are primarily related to differences in risk perception within virtual environments.