Sensitivity analysis of AI-based algorithms for autonomous driving on
  optical wavefront aberrations induced by the windshield
Dominik Werner Wolf Markus Ulrich Nikhil Kapoor
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.