SOLAS: Superpositioning an Optical Lens in Automotive Simulation

TL;DR

This paper introduces a novel pipeline using KrakenOS to model realistic optical aberrations in automotive simulation, enhancing the fidelity of synthetic data for perception testing.

Contribution

It presents a new method for degrading automotive fisheye images with optical artifacts using a Python ray-tracing library, bridging a gap in realistic simulation modeling.

Findings

KrakenOS achieves an RMSE of 0.023 compared to industry benchmark

Optical degradations affect image sharpness from periphery to center

The pipeline improves realism in synthetic automotive perception data

Abstract

Automotive Simulation is a potentially cost-effective strategy to identify and test corner case scenarios in automotive perception. Recent work has shown a significant shift in creating realistic synthetic data for road traffic scenarios using a video graphics engine. However, a gap exists in modeling realistic optical aberrations associated with cameras in automotive simulation. This paper builds on the concept from existing literature to model optical degradations in simulated environments using the Python-based ray-tracing library KrakenOS. As a novel pipeline, we degrade automotive fisheye simulation using an optical doublet with +/-2 deg Field of View (FOV), introducing realistic optical artifacts into two simulation images from SynWoodscape and Parallel Domain Woodscape. We evaluate KrakenOS by calculating the Root Mean Square Error (RMSE), which averaged around 0.023 across the RGB light spectrum compared to Ansys Zemax OpticStudio, an industrial benchmark for optical design and simulation. Lastly, we measure the image sharpness of the degraded simulation using the ISO12233:2023 Slanted Edge Method and show how both qualitative and measured results indicate the extent of the spatial variation in image sharpness from the periphery to the center of the degradations.