Fidelity requirements for simulating sensor performance in autonomous ground vehicles

Abstract

Simulation has become an important enabler in the development and testing of autonomous ground vehicles (AGV), with simulation being used both to generate training data for AI/ML-based segmentation and classification algorithms and to enable in-the-loop testing of the AGV systems that use those algorithms. Furthermore, digital twins of physical test areas provide a safe, repeatable way to conduct critical safety and performance testing of these AI/ML algorithms and their performance on AGV systems. For both these digital twins and the sensor models that use them to generate synthetic data, it is important to understand the relationship between the fidelity of the scene/model and the accuracy of the resulting synthetic sensor data. This work presents a quantitative evaluation of the relationship between digital scene fidelity, sensor model fidelity, and the quality of the resulting synthetic sensor data, with a focus on camera data typically used on AGV to enable autonomous navigation.