Sensitivity analysis of AI-based algorithms for autonomous driving on optical wavefront aberrations induced by the windshield

• URL: http://arxiv.org/abs/2308.11711v1
• Date: Sat, 19 Aug 2023 17:01:23 GMT
• Title: Sensitivity analysis of AI-based algorithms for autonomous driving on optical wavefront aberrations induced by the windshield
• Authors: Dominik Werner Wolf and Markus Ulrich and Nikhil Kapoor
• Abstract summary: This paper investigates the domain shift problem by evaluating the sensitivity of two perception models to different windshield configurations. Our results show that there is a performance gap introduced by windshields and existing optical metrics used for posing requirements might not be sufficient.
• Score: 4.542616945567623
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Autonomous driving perception techniques are typically based on supervised machine learning models that are trained on real-world street data. A typical training process involves capturing images with a single car model and windshield configuration. However, deploying these trained models on different car types can lead to a domain shift, which can potentially hurt the neural networks performance and violate working ADAS requirements. To address this issue, this paper investigates the domain shift problem further by evaluating the sensitivity of two perception models to different windshield configurations. This is done by evaluating the dependencies between neural network benchmark metrics and optical merit functions by applying a Fourier optics based threat model. Our results show that there is a performance gap introduced by windshields and existing optical metrics used for posing requirements might not be sufficient.

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