Automatic Generation and Population of a Graphics-Based Driving Simulator

Abstract

A stereo image and high-accuracy positional data were used to generate and populate a low-fidelity graphics model. The data were acquired by a simple mobile mapping system. The use of positional data made it possible to generate automatically a sparse model consisting of a road, central road marking, a green area, and a skybox. This allowed for several applications, such as the synchronization of the model with the video and the semiautomatic population of road signs into the model data. An experiment was conducted to evaluate the model and the video as viable sources for behavioral testing of drivers. The correlations between driver speed in response to the model and the video are presented; this presentation allows for an examination of the effect of the fidelity of the driving simulator's visual cue stream. The study results were used to compare driver speed in a real vehicle with driver speeds in the video and model roads, with correlations of 84.6% (between video and ground truth), 87.3% (between model and ground truth), and 92.8% (between video and model).