Simulation software company rFpro has developed an accurate virtual replica of Tokyo’s Shuto Expressway, Inner Circular route to help vehicle engineering developers.

The digital twin will accelerate the training of artificial intelligence (AI) by reducing the cost and risks of collecting real-world data.

Major vehicle manufacturers have already adopted the virtual environment to further innovate autonomous vehicles.

The C1 35km-long route Tokyo Expressway has been modelled using survey-grade LiDAR scan data to create a vehicle dynamics grade road surface.

The environment is geometrically and functionally precise, with each of the thousands of road signs, markings and roadside objects being individually classified with an accuracy of 1mm.

rFpro noted that the digital twin enables users to add intelligent traffic information to create an almost infinite number of test scenarios in this model.

The types of vehicles, their speeds, colour and density of traffic can be varied.

Furthermore, the rFpro system allows a large number of humans to drive in the model at the same time, offering a cost and time-effective way of creating large quantities of usable training data.

The digital replica includes more than 100 locations of other public road routes, proving grounds and test tracks.

rFpro managing director Matt Daley said: “The C1 route is one of the most challenging stretches of city roads in the world for an autonomous vehicle to navigate.

“With constantly changing road curvature and elevation, complex and densely situated junctions and a huge array of road signs and markings, it is the ultimate test of autonomous vehicle technologies and is the perfect way to exercise and develop such capabilities safely.

“Collecting the volume and variety of training data needed for this type of road network would be very expensive, time consuming and potentially dangerous in the real world.”