Abstract
In this study, we propose a novel approach to enrich the training data for automated driving by using a self-designed driving simulator and two human drivers to generate safety-critical corner cases in a short period of time, as already presented in [12]. Our results show that incorporating these corner cases during training improves the recognition of corner cases during testing, even though, they were recorded due to visual impairment. Using the corner case triggering pipeline developed in the previous work, we investigate the effectiveness of using expert models to overcome the domain gap due to different weather conditions and times of day, compared to a universal model from a development perspective. Our study reveals that expert models can provide significant benefits in terms of performance and efficiency, and can reduce the time and effort required for model training. Our results contribute to the progress of automated driving, providing a pathway for safer and more reliable autonomous vehicles on the road in the future.
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Acknowledgements
The research leading to these results is funded by the German Federal Ministry for Economic Affairs and Climate Action within the project KI Data Tooling under the grant number 19A20001E. We thank Matthias Rottmann for his productive support, Natalie Grabowsky and Ben Hamscher for driving the streets of CARLA.
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Kowol, K., Bracke, S., Gottschalk, H. (2023). survAIval: Survival Analysis with the Eyes of AI. In: Holzinger, A., da Silva, H.P., Vanderdonckt, J., Constantine, L. (eds) Computer-Human Interaction Research and Applications. CHIRA CHIRA 2021 2022. Communications in Computer and Information Science, vol 1882. Springer, Cham. https://doi.org/10.1007/978-3-031-41962-1_8
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