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How certain are we that our automated driving system is safe?

Erwin de GelderOlaf Op den Camp
Published in: Traffic injury prevention (2023)
If results show that the uncertainty is too high, the proposed method allows answering questions like "How much more data do we need?" or "How many more (virtual) simulations must be conducted?" Therefore, the method can be used to set requirements on the amount of data and the number of (virtual) simulations. For a reliable risk estimate, though, much more data are needed than those used in the case study. Furthermore, because the method relies on (virtual) simulations, the reliability of the result depends on the validity of the models used in the simulations. The presented case study illustrates that the proposed method is able to quantify the uncertainty of the estimated safety risk of an ADS. Future work involves incorporating the proposed method into the type approval framework for future ADSs of SAE levels 3, 4, and 5, as proposed in the upcoming European Union implementing regulation for ADS.
Keyphrases
  • electronic health record
  • monte carlo
  • current status
  • big data
  • deep learning
  • high throughput
  • data analysis
  • single cell
  • artificial intelligence