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Validating Camera Sensor Models for Virtual Testing of Vision Systems in Automated Driving

Ulreich F, Funk Drechsler M, Poledna Y, Chan PH, Herraren T, Ebert M, Kaup A, Huber W (2026)

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Publication Type: Conference contribution

Publication year: 2026

Publisher: IEEE

City/Town: New York City

Pages Range: 57-64

Conference Proceedings Title: 2025 IEEE International Conference on Vehicular Electronics and Safety (ICVES)

Event location: Coventry

DOI: 10.1109/ICVES65691.2025.11376043

Abstract

Vision systems in automated driving deliver information on which subsequent processing stages rely to perform safety-critical decisions. With the application of deep-learning models, more effort is needed to validate the sensor models used in virtual environments, as it is not known which features of the image data affect the prediction. This work proposes a validation method based on a standardized image-quality target according to ISO12233:2023 and ISO14524, together with a high-precision geometrical digital twin for direct comparison between real and simulated images taken of a driving scenario. The proposed approach is applied to the default camera sensor model in CARLA and targets the geometric and the radiometric parts separately. Thereby, this work proposes an enhanced model that fits the characteristics of the real camera used during experiments. Images generated with the proposed simulation model reduce the sim-to-real gap and are 112.73% better than the default model in terms of the Michelson-contrast. Evaluating the OECF-curves yields an 85.16% improvement. To demonstrate the applicability, we assess the performance of a deep neural network detecting a EuroNCAP pedestrian target at distances ranging from 5m to 100m. The results show that the default simulation model leads to overconfident predictions compared to the proposed model and the real camera. Furthermore, we will use the enhanced model in X-in-the-Loop tests of automated driving systems.

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APA:

Ulreich, F., Funk Drechsler, M., Poledna, Y., Chan, P.H., Herraren, T., Ebert, M.,... Huber, W. (2025). Validating Camera Sensor Models for Virtual Testing of Vision Systems in Automated Driving. In 2025 IEEE International Conference on Vehicular Electronics and Safety (ICVES) (pp. 57-64). Coventry, GB: New York City: IEEE.

MLA:

Ulreich, Fabian, et al. "Validating Camera Sensor Models for Virtual Testing of Vision Systems in Automated Driving." Proceedings of the 2025 IEEE International Conference on Vehicular Electronics and Safety (ICVES), Coventry New York City: IEEE, 2025. 57-64.

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