survAIval: Survival Analysis with the Eyes of AI
This addresses the challenge of creating reliable training data for autonomous vehicles, though it builds incrementally on prior work.
The study tackled the problem of training data scarcity for automated driving by generating safety-critical corner cases using a driving simulator with human drivers, showing that incorporating these cases improved corner case recognition during testing. They also found that expert models for specific weather/time conditions outperformed universal models in performance and efficiency.
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~\cite{kowol22simulator}. 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.