The importance of data selection and uncertainty estimation
Data is key to the development of automated driving functions. In this article, Dr. Nico Schmidt, Machine Learning Architect at CARIAD, explains how we use data sets to train machine learning models, determine uncertainty and, ultimately, develop enhanced customer features.
At CARIAD, we use data sets for training and testing machine learning models, and ensure that they’re as diverse as possible with respect to traffic scenarios, regions and environmental conditions, for example. In this way, we can cover a larger application space and test that our ADAS/AD functions work in the places and situations where we want to deploy them.
We’re particularly interested in discovering and iteratively training data that improves model performance the most. We can then gradually select new data and add it to the training set to refine our model over time. This is accompanied by corner case detection, which even ensure that we have appropriate measures once we get out of our operational domain. Ultimately, we can develop new perception functions or expand existing functions by creating new data sets with scenario-based triggers and retrieval methods.
But first of all, let’s start with the basics.
What data is useful to us?
Most of the time, we drive around in relatively similar situations on the road. We already have a lot of data from these kinds of situations and our machine learning models perform well here. What we see as more informative are rare scenarios and corner cases. These are often safety-critical scenarios where our model and vehicles have the greatest opportunity to learn.
Corner cases may involve a weak or noisy sensor signal as a result of difficult weather conditions. Alternatively, there may be a context mismatch, where our model understands the individual objects but, when it sees them together in a strange combination, gets confused. Or it may be a situation where the car encounters an object that’s very rare and underrepresented in our data set.
Join us in developing machine learning models for automated driving
At CARIAD, we’re always on the lookout for the brightest digital minds and tech experts to join our team and shape the future of automotive mobility with us. If you’re experienced in the fields of AI and data and fancy a new challenge, check out the relevant open positions below.