Low Fidelity Digital Twin for Automated Driving Systems: Use Cases and Automatic Generation

TL;DR

This paper introduces a low fidelity digital twin generator for automated driving systems, enabling automatic virtual environment creation and validation through real vehicle data replay, reducing reliance on high fidelity simulations.

Contribution

It presents a novel low fidelity digital twin generation method and discusses scenarios where automatic generation is advantageous over high fidelity simulation.

Findings

Validated approach by replaying real vehicle data

Demonstrated effectiveness in specific use cases

Highlighted benefits of low fidelity digital twins

Abstract

Automated driving systems are an integral part of the automotive industry. Tools such as Robot Operating System and simulators support their development. However, in the end, the developers must test their algorithms on a real vehicle. To better observe the difference between reality and simulation--the reality gap--digital twin technology offers real-time communication between the real vehicle and its model. We present low fidelity digital twin generator and describe situations where automatic generation is preferable to high fidelity simulation. We validated our approach of generating a virtual environment with a vehicle model by replaying the data recorded from the real vehicle.