Automatic traffic modelling for creating digital twins to facilitate autonomous vehicle development

A digital twin is often adopted in computer simulations to expedite autonomous vehicle developments by using the simulated 3D environment that reflects a physical environment. In particular, traffic simulations are a crucial part of training the driving logic before the field test of an autonomous vehicle is performed on specific regions to adapt to the region-specific, dynamic traffic conditions. Currently, the traffic conditions are either synthesised by tools (e.g. using mathematical models) or created manually (using domain knowledge), which cannot reflect the realistic, region-specific conditions or will require extensive labour works. In this article, we propose an automatic methodology to model the real-world traffic conditions captured by the sensor data and to reproduce the modeled traffic in the digital twin. We have built the tools based on the methodology and use the KITTI dataset to validate the effectiveness of the tools. To recreate the region-specific traffic, we present the results of capturing, modelling, and recreating the two-wheeler traffic condition on the Southeast Asia road. Our experimental results show that the proposed method facilitates the simulation of real-world, Southeast Asia-specific traffic conditions by removing the needs of the synthesised traffic and the labour hours.